Macromolecular modeling and design in Rosetta: recent methods and frameworks

ProteinMPNN Recovers Complex Sequence Properties of Transmembrane β-barrels

Recent deep-learning (DL) protein design methods have been successfully applied to a range of protein design problems, including the de novo design of novel folds, protein binders, and enzymes. However, DL methods have yet to meet the challenge of de novo membrane protein (MP) and the design of complex β-sheet folds. We performed a comprehensive benchmark of one DL protein sequence design method, ProteinMPNN, using transmembrane and water-soluble β-barrel folds as a model, and compared the performance of ProteinMPNN to the new membrane-specific Rosetta Franklin2023 energy function. We tested the effect of input backbone refinement on ProteinMPNN performance and found that given refined and well-defined inputs, ProteinMPNN more accurately captures global sequence properties despite complex folding biophysics. It generates more diverse TMB sequences than Franklin2023 in pore-facing positions. In addition, ProteinMPNN generated TMB sequences that passed state-of-the-art in silico filters for experimental validation, suggesting that the model could be used in de novo design tasks of diverse nanopores for single-molecule sensing and sequencing. Lastly, our results indicate that the low success rate of ProteinMPNN for the design of β-sheet proteins stems from backbone input accuracy rather than software limitations.

An efflux pump in genomic island GI-M202a mediates the transfer of polymyxin B resistance in Pandoraea pnomenusa M202

BACKGROUND

Polymyxin B is considered a last-line therapeutic option against multidrug-resistant gram-negative bacteria, especially in COVID-19 coinfections or other serious infections. However, the risk of antimicrobial resistance and its spread to the environment should be brought to the forefront.

METHODS

Pandoraea pnomenusa M202 was isolated under selection with 8 mg/L polymyxin B from hospital sewage and then was sequenced by the PacBio RS II and Illumina HiSeq 4000 platforms. Mating experiments were performed to evaluate the transfer of the major facilitator superfamily (MFS) transporter in genomic islands (GIs) to Escherichia coli 25DN. The recombinant E. coli strain Mrc-3 harboring MFS transporter encoding gene FKQ53_RS21695 was also constructed. The influence of efflux pump inhibitors (EPIs) on MICs was determined. The mechanism of polymyxin B excretion mediated by FKQ53_RS21695 was investigated by Discovery Studio 2.0 based on homology modeling.

RESULTS

The MIC of polymyxin B for the multidrug-resistant bacterial strain P. pnomenusa M202, isolated from hospital sewage, was 96 mg/L. GI-M202a, harboring an MFS transporter-encoding gene and conjugative transfer protein-encoding genes of the type IV secretion system, was identified in P. pnomenusa M202. The mating experiment between M202 and E. coli 25DN reflected the transferability of polymyxin B resistance via GI-M202a. EPI and heterogeneous expression assays also suggested that the MFS transporter gene FKQ53_RS21695 in GI-M202a was responsible for polymyxin B resistance. Molecular docking revealed that the polymyxin B fatty acyl group inserts into the hydrophobic region of the transmembrane core with Pi-alkyl and unfavorable bump interactions, and then polymyxin B rotates around Tyr43 to externally display the peptide group during the efflux process, accompanied by an inward-to-outward conformational change in the MFS transporter. Additionally, verapamil and CCCP exhibited significant inhibition via competition for binding sites.

CONCLUSIONS

These findings demonstrated that GI-M202a along with the MFS transporter FKQ53_RS21695 in P. pnomenusa M202 could mediate the transmission of polymyxin B resistance.

De novo design of high-affinity binders of bioactive helical peptides

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

Harnessing AlphaFold to reveal state secrets: Prediction of hERG closed and inactivated states

To design safe, selective, and effective new therapies, there must be a deep understanding of the structure and function of the drug target. One of the most difficult problems to solve has been resolution of discrete conformational states of transmembrane ion channel proteins. An example is KV11.1 (hERG), comprising the primary cardiac repolarizing current, IKr. hERG is a notorious drug anti-target against which all promising drugs are screened to determine potential for arrhythmia. Drug interactions with the hERG inactivated state are linked to elevated arrhythmia risk, and drugs may become trapped during channel closure. However, the structural details of multiple conformational states have remained elusive. Here, we guided AlphaFold2 to predict plausible hERG inactivated and closed conformations, obtaining results consistent with myriad available experimental data. Drug docking simulations demonstrated hERG state-specific drug interactions aligning well with experimental results, revealing that most drugs bind more effectively in the inactivated state and are trapped in the closed state. Molecular dynamics simulations demonstrated ion conduction that aligned with earlier studies. Finally, we identified key molecular determinants of state transitions by analyzing interaction networks across closed, open, and inactivated states in agreement with earlier mutagenesis studies. Here, we demonstrate a readily generalizable application of AlphaFold2 as a novel method to predict discrete protein conformations and novel linkages from structure to function.

TIMED-Design: flexible and accessible protein sequence design with convolutional neural networks

Sequence design is a crucial step in the process of designing or engineering proteins. Traditionally, physics-based methods have been used to solve for optimal sequences, with the main disadvantages being that they are computationally intensive for the end user. Deep learning-based methods offer an attractive alternative, outperforming physics-based methods at a significantly lower computational cost. In this paper, we explore the application of Convolutional Neural Networks (CNNs) for sequence design. We describe the development and benchmarking of a range of networks, as well as reimplementations of previously described CNNs. We demonstrate the flexibility of representing proteins in a three-dimensional voxel grid by encoding additional design constraints into the input data. Finally, we describe TIMED-Design, a web application and command line tool for exploring and applying the models described in this paper. The user interface will be available at the URL: https://pragmaticproteindesign.bio.ed.ac.uk/timed. The source code for TIMED-Design is available at https://github.com/wells-wood-research/timed-design.

Computational Design of Peptide Assemblies

With the ongoing development of peptide self-assembling materials, there is growing interest in exploring novel functional peptide sequences. From short peptides to long polypeptides, as the functionality increases, the sequence space is also expanding exponentially. Consequently, attempting to explore all functional sequences comprehensively through experience and experiments alone has become impractical. By utilizing computational methods, especially artificial intelligence enhanced molecular dynamics (MD) simulation and de novo peptide design, there has been a significant expansion in the exploration of sequence space. Through these methods, a variety of supramolecular functional materials, including fibers, two-dimensional arrays, nanocages, etc., have been designed by meticulously controlling the inter- and intramolecular interactions. In this review, we first provide a brief overview of the current main computational methods and then focus on the computational design methods for various self-assembled peptide materials. Additionally, we introduce some representative protein self-assemblies to offer guidance for the design of self-assembling peptides.

[Bioinformatics analysis and validation of the interaction between PML protein and TAB1 protein]

OBJECTIVE

To analyze the interaction between PML protein and TAB1 protein using bioinformatic approaches and experimentally verify the results.

METHODS

Using Rosetta software, a 3D model of TAB1 protein was constructed through a comparative modeling approach; the secondary structure of PML protein was retrieved in the PDB database and its crystal structure and 3D structure were resolved. Zdock 3.0.2 software was used to perform protein-protein docking of PML and TAB1, and the best conformation was extracted for molecular structure analysis of the docking model. The interaction between the two proteins was detected using immunoprecipitation in α-MMC-treated M1 inflammatory macrophages.

RESULTS

When 6IMQ of PML was used as the docking site, PML protein formed 3 salt bridges, 6 hydrogen bonds and 6 hydrophobic interactions with TAB1 proteins; when 5YUF of PML was used as the docking site, PML protein formed 1 hydrogen bond, 3 electrostatic interactions and 9 hydrophobic interactions with TAB1 proteins, and both of the docking modes formed good molecular docking and interactions. In the M1 inflammatory macrophages treated with α-MMC for 4 h, positive protein bands of PML and TAB1 were detected in the cell lysates in PML-IP group.

CONCLUSION

PML protein can interact strongly with TAB1 protein.

Ferric reduction by a CYBDOM protein counteracts increased iron availability in root meristems induced by phosphorus deficiency

To mobilize sparingly available phosphorus (P) in the rhizosphere, many plant species secrete malate to release P sorbed onto (hydr)oxides of aluminum and iron (Fe). In the presence of Fe, malate can provoke Fe over-accumulation in the root apoplast, triggering a series of events that inhibit root growth. Here, we identified HYPERSENSITIVE TO LOW P1 (HYP1), a CYBDOM protein constituted of a DOMON and a cytochrome b561 domain, as critical to maintain cell elongation and meristem integrity under low P. We demonstrate that HYP1 mediates ascorbate-dependent trans-plasma membrane electron transport and can reduce ferric and cupric substrates in Xenopus laevis oocytes and in planta. HYP1 expression is up-regulated in response to P deficiency in the proximal zone of the root apical meristem. Disruption of HYP1 leads to increased Fe and callose accumulation in the root meristem and causes significant transcriptional changes in roots. We further demonstrate that HYP1 activity overcomes malate-induced Fe accumulation, thereby preventing Fe-dependent root growth arrest in response to low P. Collectively, our results uncover an ascorbate-dependent metalloreductase that is critical to protect root meristems of P-deficient plants from increased Fe availability and provide insights into the physiological function of the yet poorly characterized but ubiquitous CYBDOM proteins.

A Perspective on the Prospective Use of AI in Protein Structure Prediction

AlphaFold2 (AF2) and RoseTTaFold (RF) have revolutionized structural biology, serving as highly reliable and effective methods for predicting protein structures. This article explores their impact and limitations, focusing on their integration into experimental pipelines and their application in diverse protein classes, including membrane proteins, intrinsically disordered proteins (IDPs), and oligomers. In experimental pipelines, AF2 models help X-ray crystallography in resolving the phase problem, while complementarity with mass spectrometry and NMR data enhances structure determination and protein flexibility prediction. Predicting the structure of membrane proteins remains challenging for both AF2 and RF due to difficulties in capturing conformational ensembles and interactions with the membrane. Improvements in incorporating membrane-specific features and predicting the structural effect of mutations are crucial. For intrinsically disordered proteins, AF2's confidence score (pLDDT) serves as a competitive disorder predictor, but integrative approaches including molecular dynamics (MD) simulations or hydrophobic cluster analyses are advocated for accurate dynamics representation. AF2 and RF show promising results for oligomeric models, outperforming traditional docking methods, with AlphaFold-Multimer showing improved performance. However, some caveats remain in particular for membrane proteins. Real-life examples demonstrate AF2's predictive capabilities in unknown protein structures, but models should be evaluated for their agreement with experimental data. Furthermore, AF2 models can be used complementarily with MD simulations. In this Perspective, we propose a "wish list" for improving deep-learning-based protein folding prediction models, including using experimental data as constraints and modifying models with binding partners or post-translational modifications. Additionally, a meta-tool for ranking and suggesting composite models is suggested, driving future advancements in this rapidly evolving field.

Targeting ion channels with ultra-large library screening for hit discovery

Ion channels play a crucial role in a variety of physiological and pathological processes, making them attractive targets for drug development in diseases such as diabetes, epilepsy, hypertension, cancer, and chronic pain. Despite the importance of ion channels in drug discovery, the vastness of chemical space and the complexity of ion channels pose significant challenges for identifying drug candidates. The use of in silico methods in drug discovery has dramatically reduced the time and cost of drug development and has the potential to revolutionize the field of medicine. Recent advances in computer hardware and software have enabled the screening of ultra-large compound libraries. Integration of different methods at various scales and dimensions is becoming an inevitable trend in drug development. In this review, we provide an overview of current state-of-the-art computational chemistry methodologies for ultra-large compound library screening and their application to ion channel drug discovery research. We discuss the advantages and limitations of various in silico techniques, including virtual screening, molecular mechanics/dynamics simulations, and machine learning-based approaches. We also highlight several successful applications of computational chemistry methodologies in ion channel drug discovery and provide insights into future directions and challenges in this field.

Programmable synthetic receptors: the next-generation of cell and gene therapies

Cell and gene therapies hold tremendous promise for treating a range of difficult-to-treat diseases. However, concerns over the safety and efficacy require to be further addressed in order to realize their full potential. Synthetic receptors, a synthetic biology tool that can precisely control the function of therapeutic cells and genetic modules, have been rapidly developed and applied as a powerful solution. Delicately designed and engineered, they can be applied to finetune the therapeutic activities, i.e., to regulate production of dosed, bioactive payloads by sensing and processing user-defined signals or biomarkers. This review provides an overview of diverse synthetic receptor systems being used to reprogram therapeutic cells and their wide applications in biomedical research. With a special focus on four synthetic receptor systems at the forefront, including chimeric antigen receptors (CARs) and synthetic Notch (synNotch) receptors, we address the generalized strategies to design, construct and improve synthetic receptors. Meanwhile, we also highlight the expanding landscape of therapeutic applications of the synthetic receptor systems as well as current challenges in their clinical translation.

Applications of Molecular Dynamics Simulations in Drug Discovery

In the current drug development process, molecular dynamics (MD) simulations have proven to be very useful. This chapter provides an overview of the current applications of MD simulations in drug discovery, from detecting protein druggable sites and validating drug docking outcomes to exploring protein conformations and investigating the influence of mutations on its structure and functions. In addition, this chapter emphasizes various strategies to improve the conformational sampling efficiency in molecular dynamics simulations. With a growing computer power and developments in the production of force fields and MD techniques, the importance of MD simulations in helping the drug development process is projected to rise significantly in the future.

Revolutionizing Peptide-Based Drug Discovery: Advances in the Post-AlphaFold Era

Peptide-based drugs offer high specificity, potency, and selectivity. However, their inherent flexibility and differences in conformational preferences between their free and bound states create unique challenges that have hindered progress in effective drug discovery pipelines. The emergence of AlphaFold (AF) and Artificial Intelligence (AI) presents new opportunities for enhancing peptide-based drug discovery. We explore recent advancements that facilitate a successful peptide drug discovery pipeline, considering peptides' attractive therapeutic properties and strategies to enhance their stability and bioavailability. AF enables efficient and accurate prediction of peptide-protein structures, addressing a critical requirement in computational drug discovery pipelines. In the post-AF era, we are witnessing rapid progress with the potential to revolutionize peptide-based drug discovery such as the ability to rank peptide binders or classify them as binders/non-binders and the ability to design novel peptide sequences. However, AI-based methods are struggling due to the lack of well-curated datasets, for example to accommodate modified amino acids or unconventional cyclization. Thus, physics-based methods, such as docking or molecular dynamics simulations, continue to hold a complementary role in peptide drug discovery pipelines. Moreover, MD-based tools offer valuable insights into binding mechanisms, as well as the thermodynamic and kinetic properties of complexes. As we navigate this evolving landscape, a synergistic integration of AI and physics-based methods holds the promise of reshaping the landscape of peptide-based drug discovery.

The PfRCR complex bridges malaria parasite and erythrocyte during invasion

The symptoms of malaria occur during the blood stage of infection, when parasites invade and replicate within human erythrocytes. The PfPCRCR complex1, containing PfRH5 (refs. 2,3), PfCyRPA, PfRIPR, PfCSS and PfPTRAMP, is essential for erythrocyte invasion by the deadliest human malaria parasite, Plasmodium falciparum. Invasion can be prevented by antibodies3-6 or nanobodies1 against each of these conserved proteins, making them the leading blood-stage malaria vaccine candidates. However, little is known about how PfPCRCR functions during invasion. Here we present the structure of the PfRCR complex7,8, containing PfRH5, PfCyRPA and PfRIPR, determined by cryogenic-electron microscopy. We test the hypothesis that PfRH5 opens to insert into the membrane9, instead showing that a rigid, disulfide-locked PfRH5 can mediate efficient erythrocyte invasion. We show, through modelling and an erythrocyte-binding assay, that PfCyRPA-binding antibodies5 neutralize invasion through a steric mechanism. We determine the structure of PfRIPR, showing that it consists of an ordered, multidomain core flexibly linked to an elongated tail. We also show that the elongated tail of PfRIPR, which is the target of growth-neutralizing antibodies6, binds to the PfCSS-PfPTRAMP complex on the parasite membrane. A modular PfRIPR is therefore linked to the merozoite membrane through an elongated tail, and its structured core presents PfCyRPA and PfRH5 to interact with erythrocyte receptors. This provides fresh insight into the molecular mechanism of erythrocyte invasion and opens the way to new approaches in rational vaccine design.

Engineering status of protein for improving microbial cell factories

With the development of metabolic engineering and synthetic biology, microbial cell factories (MCFs) have provided an efficient and sustainable method to synthesize a series of chemicals from renewable feedstocks. However, the efficiency of MCFs is usually limited by the inappropriate status of protein. Thus, engineering status of protein is essential to achieve efficient bioproduction with high titer, yield and productivity. In this review, we summarize the engineering strategies for metabolic protein status, including protein engineering for boosting microbial catalytic efficiency, protein modification for regulating microbial metabolic capacity, and protein assembly for enhancing microbial synthetic capacity. Finally, we highlight future challenges and prospects of improving microbial cell factories by engineering status of protein.

Evaluation of AlphaFold antibody-antigen modeling with implications for improving predictive accuracy

High resolution antibody-antigen structures provide critical insights into immune recognition and can inform therapeutic design. The challenges of experimental structural determination and the diversity of the immune repertoire underscore the necessity of accurate computational tools for modeling antibody-antigen complexes. Initial benchmarking showed that despite overall success in modeling protein-protein complexes, AlphaFold and AlphaFold-Multimer have limited success in modeling antibody-antigen interactions. In this study, we performed a thorough analysis of AlphaFold's antibody-antigen modeling performance on 427 nonredundant antibody-antigen complex structures, identifying useful confidence metrics for predicting model quality, and features of complexes associated with improved modeling success. Notably, we found that the latest version of AlphaFold improves near-native modeling success to over 30%, versus approximately 20% for a previous version, while increased AlphaFold sampling gives approximately 50% success. With this improved success, AlphaFold can generate accurate antibody-antigen models in many cases, while additional training or other optimization may further improve performance.

Immunoinformatics-based potential multi-peptide vaccine designing against Jamestown Canyon Virus (JCV) capable of eliciting cellular and humoral immune responses

Jamestown Canyon virus (JCV) is a deadly viral infection transmitted by various mosquito species. This mosquito-borne virus belongs to Bunyaviridae family, posing a high public health threat in the in tropical regions of the United States causing encephalitis in humans. Common symptoms of JCV include fever, headache, stiff neck, photophobia, nausea, vomiting, and seizures. Despite the availability of resources, there is currently no vaccine or drug available to combat JCV. The purpose of this study was to develop an epitope-based vaccine using immunoinformatics approaches. The vaccine aimed to be secure, efficient, bio-compatible, and capable of stimulating both innate and adaptive immune responses. In this study, the protein sequence of JCV was obtained from the NCBI database. Various bioinformatics methods, including toxicity evaluation, antigenicity testing, conservancy analysis, and allergenicity assessment were utilized to identify the most promising epitopes. Suitable linkers and adjuvant sequences were used in the design of vaccine construct. 50s ribosomal protein sequence was used as an adjuvant at the N-terminus of the construct. A total of 5 CTL, 5 HTL, and 5 linear B cell epitopes were selected based on non-allergenicity, immunological potential, and antigenicity scores to design a highly immunogenic multi-peptide vaccine construct. Strong interactions between the proposed vaccine and human immune receptors, i.e., TLR-2 and TLR-4, were revealed in a docking study using ClusPro software, suggesting their possible relevance in the immunological response to the vaccine. Immunological and physicochemical properties assessment ensured that the proposed vaccine demonstrated high immunogenicity, solubility and thermostability. Molecular dynamics simulations confirmed the strong binding affinities, as well as dynamic and structural stability of the proposed vaccine. Immune simulation suggest that the vaccine has the potential to effectively stimulate cellular and humoral immune responses to combat JCV infection. Experimental and clinical assays are required to validate the results of this study.

Antigen spacing on protein nanoparticles influences antibody responses to vaccination

Immunogen design approaches aim to control the specificity and quality of antibody responses elicited by next-generation vaccines. Here, we use computational protein design to generate a nanoparticle vaccine platform based on the receptor-binding domain (RBD) of influenza hemagglutinin (HA) that enables precise control of antigen conformation and spacing. HA RBDs are presented as either monomers or native-like closed trimers that are connected to the underlying nanoparticle by a rigid linker that is modularly extended to precisely control antigen spacing. Nanoparticle immunogens with decreased spacing between trimeric RBDs elicit antibodies with improved hemagglutination inhibition and neutralization potency as well as binding breadth across diverse H1 HAs. Our "trihead" nanoparticle immunogen platform provides insights into anti-HA immunity, establishes antigen spacing as an important parameter in structure-based vaccine design, and embodies several design features that could be used in next-generation vaccines against influenza and other viruses.

Combinatorial immune refocusing within the influenza hemagglutinin RBD improves cross-neutralizing antibody responses

The receptor-binding domain (RBD) of influenza virus hemagglutinin (HA) elicits potently neutralizing yet mostly strain-specific antibodies. Here, we evaluate the ability of several immunofocusing techniques to enhance the functional breadth of vaccine-elicited immune responses against the HA RBD. We present a series of "trihead" nanoparticle immunogens that display native-like closed trimeric RBDs from the HAs of several H1N1 influenza viruses. The series includes hyperglycosylated and hypervariable variants that incorporate natural and designed sequence diversity at key positions in the receptor-binding site periphery. Nanoparticle immunogens displaying triheads or hyperglycosylated triheads elicit higher hemagglutination inhibition (HAI) and neutralizing activity than the corresponding immunogens lacking either trimer-stabilizing mutations or hyperglycosylation. By contrast, mosaic nanoparticle display and antigen hypervariation do not significantly alter the magnitude or breadth of vaccine-elicited antibodies. Our results yield important insights into antibody responses against the RBD and the ability of several structure-based immunofocusing techniques to influence vaccine-elicited antibody responses.

Advances in ligand-specific biosensing for structurally similar molecules

The specificity of biological systems makes it possible to develop biosensors targeting specific metabolites, toxins, and pollutants in complex medical or environmental samples without interference from structurally similar compounds. For the last two decades, great efforts have been devoted to creating proteins or nucleic acids with novel properties through synthetic biology strategies. Beyond augmenting biocatalytic activity, expanding target substrate scopes, and enhancing enzymes' enantioselectivity and stability, an increasing research area is the enhancement of molecular specificity for genetically encoded biosensors. Here, we summarize recent advances in the development of highly specific biosensor systems and their essential applications. First, we describe the rational design principles required to create libraries containing potential mutants with less promiscuity or better specificity. Next, we review the emerging high-throughput screening techniques to engineer biosensing specificity for the desired target. Finally, we examine the computer-aided evaluation and prediction methods to facilitate the construction of ligand-specific biosensors.

Sculpting conducting nanopore size and shape through de novo protein design

Transmembrane β-barrels (TMBs) are widely used for single molecule DNA and RNA sequencing and have considerable potential for a broad range of sensing and sequencing applications. Current engineering approaches for nanopore sensors are limited to naturally occurring channels such as CsgG, which have evolved to carry out functions very different from sensing, and hence provide sub-optimal starting points. In contrast, de novo protein design can in principle create an unlimited number of new nanopores with any desired properties. Here we describe a general approach to the design of transmembrane β-barrel pores with different diameter and pore geometry. NMR and crystallographic characterization shows that the designs are stably folded with structures close to the design models. We report the first examples of de novo designed TMBs with 10, 12 and 14 stranded β-barrels. The designs have distinct conductances that correlate with their pore diameter, ranging from 110 pS (~0.5 nm pore diameter) to 430 pS (~1.1 nm pore diameter), and can be converted into sensitive small-molecule sensors with high signal to noise ratio. The capability to generate on demand β-barrel pores of defined geometry opens up fundamentally new opportunities for custom engineering of sequencing and sensing technologies.

Directing polymorph specific calcium carbonate formation with de novo protein templates

Biomolecules modulate inorganic crystallization to generate hierarchically structured biominerals, but the atomic structure of the organic-inorganic interfaces that regulate mineralization remain largely unknown. We hypothesized that heterogeneous nucleation of calcium carbonate could be achieved by a structured flat molecular template that pre-organizes calcium ions on its surface. To test this hypothesis, we design helical repeat proteins (DHRs) displaying regularly spaced carboxylate arrays on their surfaces and find that both protein monomers and protein-Ca2+ supramolecular assemblies directly nucleate nano-calcite with non-natural {110} or {202} faces while vaterite, which forms first in the absence of the proteins, is bypassed. These protein-stabilized nanocrystals then assemble by oriented attachment into calcite mesocrystals. We find further that nanocrystal size and polymorph can be tuned by varying the length and surface chemistry of the designed protein templates. Thus, bio-mineralization can be programmed using de novo protein design, providing a route to next-generation hybrid materials.

Accurate prediction of protein assembly structure by combining AlphaFold and symmetrical docking

AlphaFold can predict the structures of monomeric and multimeric proteins with high accuracy but has a limit on the number of chains and residues it can fold. Here we show that a combination of AlphaFold and all-atom symmetric docking simulations enables highly accurate prediction of the structure of complex symmetrical assemblies. We present a method to predict the structure of complexes with cubic - tetrahedral, octahedral and icosahedral - symmetry from sequence. Focusing on proteins where AlphaFold can make confident predictions on the subunit structure, 27 cubic systems were assembled with a median TM-score of 0.99 and a DockQ score of 0.72. 21 had TM-scores of above 0.9 and were categorized as acceptable- to high-quality according to DockQ. The resulting models are energetically optimized and can be used for detailed studies of intermolecular interactions in higher-order symmetrical assemblies. The results demonstrate how explicit treatment of structural symmetry can significantly expand the size and complexity of AlphaFold predictions.

Riding the wave of innovation: immunoinformatics in fish disease control

The spread of infectious illnesses has been a significant factor restricting aquaculture production. To maximise aquatic animal health, vaccination tactics are very successful and cost-efficient for protecting fish and aquaculture animals against many disease pathogens. However, due to the increasing number of immunological cases and their complexity, it is impossible to manage, analyse, visualise, and interpret such data without the assistance of advanced computational techniques. Hence, the use of immunoinformatics tools is crucial, as they not only facilitate the management of massive amounts of data but also greatly contribute to the creation of fresh hypotheses regarding immune responses. In recent years, advances in biotechnology and immunoinformatics have opened up new research avenues for generating novel vaccines and enhancing existing vaccinations against outbreaks of infectious illnesses, thereby reducing aquaculture losses. This review focuses on understanding in silico epitope-based vaccine design, the creation of multi-epitope vaccines, the molecular interaction of immunogenic vaccines, and the application of immunoinformatics in fish disease based on the frequency of their application and reliable results. It is believed that it can bridge the gap between experimental and computational approaches and reduce the need for experimental research, so that only wet laboratory testing integrated with in silico techniques may yield highly promising results and be useful for the development of vaccines for fish.

Tertiary structure assessment at CASP15

The results of tertiary structure assessment at CASP15 are reported. For the first time, recognizing the outstanding performance of AlphaFold 2 (AF2) at CASP14, all single-chain predictions were assessed together, irrespective of whether a template was available. At CASP15, there was no single stand-out group, with most of the best-scoring groups-led by PEZYFoldings, UM-TBM, and Yang Server-employing AF2 in one way or another. Many top groups paid special attention to generating deep Multiple Sequence Alignments (MSAs) and testing variant MSAs, thereby allowing them to successfully address some of the hardest targets. Such difficult targets, as well as lacking templates, were typically proteins with few homologues. Local divergence between prediction and target correlated with localization at crystal lattice or chain interfaces, and with regions exhibiting high B-factor factors in crystal structure targets, and should not necessarily be considered as representing error in the prediction. However, analysis of exposed and buried side chain accuracy showed room for improvement even in the latter. Nevertheless, a majority of groups produced high-quality predictions for most targets, which are valuable for experimental structure determination, functional analysis, and many other tasks across biology. These include those applying methods similar to those used to generate major resources such as the AlphaFold Protein Structure Database and the ESM Metagenomic atlas: the confidence estimates of the former were also notably accurate.

Time-resolved live-cell spectroscopy reveals EphA2 multimeric assembly

Ephrin type-A receptor 2 (EphA2) is a receptor tyrosine kinase that initiates both ligand-dependent tumor-suppressive and ligand-independent oncogenic signaling. We used time-resolved, live-cell fluorescence spectroscopy to show that the ligand-free EphA2 assembles into multimers driven by two types of intermolecular interactions in the ectodomain. The first type entails extended symmetric interactions required for ligand-induced receptor clustering and tumor-suppressive signaling that inhibits activity of the oncogenic extracellular signal-regulated kinase (ERK) and protein kinase B (AKT) protein kinases and suppresses cell migration. The second type is an asymmetric interaction between the amino terminus and the membrane proximal domain of the neighboring receptors, which supports oncogenic signaling and promotes migration in vitro and tumor invasiveness in vivo. Our results identify the molecular interactions that drive the formation of the EphA2 multimeric signaling clusters and reveal the pivotal role of EphA2 assembly in dictating its opposing functions in oncogenesis.

A simple method for developing lysine targeted covalent protein reagents

Peptide-based covalent probes can target shallow protein surfaces not typically addressable using small molecules, yet there is a need for versatile approaches to convert native peptide sequences into covalent binders that can target a broad range of residues. Here we report protein-based thio-methacrylate esters-electrophiles that can be installed easily on unprotected peptides and proteins via cysteine side chains, and react efficiently and selectively with cysteine and lysine side chains on the target. Methacrylate phosphopeptides derived from 14-3-3-binding proteins irreversibly label 14-3-3σ via either lysine or cysteine residues, depending on the position of the electrophile. Methacrylate peptides targeting a conserved lysine residue exhibit pan-isoform binding of 14-3-3 proteins both in lysates and in extracellular media. Finally, we apply this approach to develop protein-based covalent binders. A methacrylate-modified variant of the colicin E9 immunity protein irreversibly binds to the E9 DNAse, resulting in significantly higher thermal stability relative to the non-covalent complex. Our approach offers a simple and versatile route to convert peptides and proteins into potent covalent binders.

Microproteins-Discovery, structure, and function

Advances in proteogenomic technologies have revealed hundreds to thousands of translated small open reading frames (sORFs) that encode microproteins in genomes across evolutionary space. While many microproteins have now been shown to play critical roles in biology and human disease, a majority of recently identified microproteins have little or no experimental evidence regarding their functionality. Computational tools have some limitations for analysis of short, poorly conserved microprotein sequences, so additional approaches are needed to determine the role of each member of this recently discovered polypeptide class. A currently underexplored avenue in the study of microproteins is structure prediction and determination, which delivers a depth of functional information. In this review, we provide a brief overview of microprotein discovery methods, then examine examples of microprotein structures (and, conversely, intrinsic disorder) that have been experimentally determined using crystallography, cryo-electron microscopy, and NMR, which provide insight into their molecular functions and mechanisms. Additionally, we discuss examples of predicted microprotein structures that have provided insight or context regarding their function. Analysis of microprotein structure at the angstrom level, and confirmation of predicted structures, therefore, has potential to identify translated microproteins that are of biological importance and to provide molecular mechanism for their in vivo roles.

Cell phenotypes can be predicted from propensities of protein conformations

Proteins exist as dynamic conformational ensembles. Here we suggest that the propensities of the conformations can be predictors of cell function. The conformational states that the molecules preferentially visit can be viewed as phenotypic determinants, and their mutations work by altering the relative propensities, thus the cell phenotype. Our examples include (i) inactive state variants harboring cancer driver mutations that present active state-like conformational features, as in K-Ras4BG12V compared to other K-Ras4BG12X mutations; (ii) mutants of the same protein presenting vastly different phenotypic and clinical profiles: cancer and neurodevelopmental disorders; (iii) alterations in the occupancies of the conformational (sub)states influencing enzyme reactivity. Thus, protein conformational propensities can determine cell fate. They can also suggest the allosteric drugs efficiency.

Accurate computational design of three-dimensional protein crystals

Protein crystallization plays a central role in structural biology. Despite this, the process of crystallization remains poorly understood and highly empirical, with crystal contacts, lattice packing arrangements and space group preferences being largely unpredictable. Programming protein crystallization through precisely engineered side-chain-side-chain interactions across protein-protein interfaces is an outstanding challenge. Here we develop a general computational approach for designing three-dimensional protein crystals with prespecified lattice architectures at atomic accuracy that hierarchically constrains the overall number of degrees of freedom of the system. We design three pairs of oligomers that can be individually purified, and upon mixing, spontaneously self-assemble into >100 µm three-dimensional crystals. The structures of these crystals are nearly identical to the computational design models, closely corresponding in both overall architecture and the specific protein-protein interactions. The dimensions of the crystal unit cell can be systematically redesigned while retaining the space group symmetry and overall architecture, and the crystals are extremely porous and highly stable. Our approach enables the computational design of protein crystals with high accuracy, and the designed protein crystals, which have both structural and assembly information encoded in their primary sequences, provide a powerful platform for biological materials engineering.

Distinct activation mechanisms of β-arrestin-1 revealed by 19F NMR spectroscopy

β-Arrestins (βarrs) are functionally versatile proteins that play critical roles in the G-protein-coupled receptor (GPCR) signaling pathways. While it is well established that the phosphorylated receptor tail plays a central role in βarr activation, emerging evidence highlights the contribution from membrane lipids. However, detailed molecular mechanisms of βarr activation by different binding partners remain elusive. In this work, we present a comprehensive study of the structural changes in critical regions of βarr1 during activation using 19F NMR spectroscopy. We show that phosphopeptides derived from different classes of GPCRs display different βarr1 activation abilities, whereas binding of the membrane phosphoinositide PIP2 stabilizes a distinct partially activated conformational state. Our results further unveil a sparsely-populated activation intermediate as well as complex cross-talks between different binding partners, implying a highly multifaceted conformational energy landscape of βarr1 that can be intricately modulated during signaling.

Insights into the recognition mechanism in the UBR box of UBR4 for its specific substrates

The N-end rule pathway is a proteolytic system involving the destabilization of N-terminal amino acids, known as N-degrons, which are recognized by N-recognins. Dysregulation of the N-end rule pathway results in the accumulation of undesired proteins, causing various diseases. The E3 ligases of the UBR subfamily recognize and degrade N-degrons through the ubiquitin-proteasome system. Herein, we investigated UBR4, which has a distinct mechanism for recognizing type-2 N-degrons. Structural analysis revealed that the UBR box of UBR4 differs from other UBR boxes in the N-degron binding sites. It recognizes type-2 N-terminal amino acids containing an aromatic ring and type-1 N-terminal arginine through two phenylalanines on its hydrophobic surface. We also characterized the binding mechanism for the second ligand residue. This is the report on the structural basis underlying the recognition of type-2 N-degrons by the UBR box with implications for understanding the N-end rule pathway.

PSMC5 regulates microglial polarization and activation in LPS-induced cognitive deficits and motor impairments by interacting with TLR4

Luteolin is a flavonoid found in high concentrations in celery and green pepper, and acts as a neuroprotectant. PSMC5 (proteasome 26S subunit, ATPase 5) protein levels were reduced after luteolin stimulation in activated microglia. We aimed to determine whether regulating PSMC5 expression could inhibit neuroinflammation, and investigate the underlying mechanisms.BV2 microglia were transfected with siRNA PSMC5 before the addition of LPS (lipopolysaccharide, 1.0 µg/ml) for 24 h in serum free DMEM. A mouse model of LPS-induced cognitive and motor impairment was established to evaluate the neuroprotective effects of shRNA PSMC5. Intracerebroventricular administration of shRNA PSMC5 was commenced 7 days prior to i.p. injection of LPS (750 μg/kg). Treatments and behavioral experiments were performed once daily for 7 consecutive days. Behavioral tests and pathological/biochemical assays were performed to evaluate LPS-induced hippocampal damage. Molecular dynamics simulation was used to confirm the interaction between PSMC5 and TLR4 (Toll-like receptor 4) in LPS-stimulated BV2 microglia. SiRNA PSMC5 inhibited BV2 microglial activation, and suppressed the release of inflammatory factors (IL-1β, COX-2, PGE2, TNF-α, and iNOS) upon after LPS stimulation in BV2 microglia. LPS increased IκB-α and p65 phosphorylation, which was attenuated by siRNA PSMC5. Behavioral tests and pathological/biochemical assays showed that shRNA PSMC5 attenuated LPS-induced cognitive and motor impairments, and restored synaptic ultrastructure and protein levels in mice. ShRNA PSMC5 reduced pro-inflammatory cytokine (TNF-α, IL-1β, PGE2, and NO) levels in the serum and brain, and relevant protein factors (iNOS and COX-2) in the brain. Furthermore, shRNA PSMC5 upregulated the anti-inflammatory mediators interleukin IL-4 and IL-10 in the serum and brain, and promoted a pro-inflammation-to-anti-inflammation phenotype shift in microglial polarization. Mechanistically, shRNA PSMC5 significantly alleviated LPS-induced TLR4 expression. The polarization of LPS-induced microglial pro-inflammation phenotype was abolished by TLR4 inhibitor and in the TLR-4-/- mouse, as in shRNA PSMC5 treatment. PSMC5 interacted with TLR4 via the amino sites Glu284, Met139, Leu127, and Phe283. PSMC5 site mutations attenuated neuroinflammation and reduced pro-inflammatory factors by reducing TLR4-related effects, thereby reducing TLR4-mediated MyD88 (myeloid differentiation factor 88)-dependent activation of NF-κB. PSMC5 could be an important therapeutic target for treatment of neurodegenerative diseases involving neuroinflammation-associated cognitive deficits and motor impairments induced by microglial activation.

Simultaneous selection of nanobodies for accessible epitopes on immune cells in the tumor microenvironment

In the rapidly advancing field of synthetic biology, there exists a critical need for technology to discover targeting moieties for therapeutic biologics. Here we present INSPIRE-seq, an approach that utilizes a nanobody library and next-generation sequencing to identify nanobodies selected for complex environments. INSPIRE-seq enables the parallel enrichment of immune cell-binding nanobodies that penetrate the tumor microenvironment. Clone enrichment and specificity vary across immune cell subtypes in the tumor, lymph node, and spleen. INSPIRE-seq identifies a dendritic cell binding clone that binds PHB2. Single-cell RNA sequencing reveals a connection with cDC1s, and immunofluorescence confirms nanobody-PHB2 colocalization along cell membranes. Structural modeling and docking studies assist binding predictions and will guide nanobody selection. In this work, we demonstrate that INSPIRE-seq offers an unbiased approach to examine complex microenvironments and assist in the development of nanobodies, which could serve as active drugs, modified to become drugs, or used as targeting moieties.

De novo Design of Peptides that Bind Specific Conformers of α-Synuclein

Insoluble amyloids rich in cross-β fibrils are observed in a number of neurodegenerative diseases. Depending on the clinicopathology, the amyloids can adopt distinct supramolecular assemblies, termed conformational strains. However, rapid methods to study amyloid in a conformationally specific manner are lacking. We introduce a novel computational method for de novo design of peptides that tile the surface of α-synuclein fibrils in a conformationally specific manner. Our method begins by identifying surfaces that are unique to the conformational strain of interest, which becomes a "target backbone" for the design of a peptide binder. Next, we interrogate structures in the PDB database with high geometric complementarity to the target. Then, we identify secondary structural motifs that interact with this target backbone in a favorable, highly occurring geometry. This method produces monomeric helical motifs with a favorable geometry for interaction with the strands of the underlying amyloid. Each motif is then symmetrically replicated to form a monolayer that tiles the amyloid surface. Finally, amino acid sequences of the peptide binders are computed to provide a sequence with high geometric and physicochemical complementarity to the target amyloid. This method was applied to a conformational strain of α-synuclein fibrils, resulting in a peptide with high specificity for the target relative to other amyloids formed by α-synuclein, tau, or Aβ40. This designed peptide also markedly slowed the formation of α-synuclein amyloids. Overall, this method offers a new tool for examining conformational strains of amyloid proteins.

A new age in protein design empowered by deep learning

The rapid progress in the field of deep learning has had a significant impact on protein design. Deep learning methods have recently produced a breakthrough in protein structure prediction, leading to the availability of high-quality models for millions of proteins. Along with novel architectures for generative modeling and sequence analysis, they have revolutionized the protein design field in the past few years remarkably by improving the accuracy and ability to identify novel protein sequences and structures. Deep neural networks can now learn and extract the fundamental features of protein structures, predict how they interact with other biomolecules, and have the potential to create new effective drugs for treating disease. As their applicability in protein design is rapidly growing, we review the recent developments and technology in deep learning methods and provide examples of their performance to generate novel functional proteins.

Structural modeling of hERG channel-drug interactions using Rosetta

The human ether-a-go-go-related gene (hERG) not only encodes a potassium-selective voltage-gated ion channel essential for normal electrical activity in the heart but is also a major drug anti-target. Genetic hERG mutations and blockage of the channel pore by drugs can cause long QT syndrome, which predisposes individuals to potentially deadly arrhythmias. However, not all hERG-blocking drugs are proarrhythmic, and their differential affinities to discrete channel conformational states have been suggested to contribute to arrhythmogenicity. We used Rosetta electron density refinement and homology modeling to build structural models of open-state hERG channel wild-type and mutant variants (Y652A, F656A, and Y652A/F656 A) and a closed-state wild-type channel based on cryo-electron microscopy structures of hERG and EAG1 channels. These models were used as protein targets for molecular docking of charged and neutral forms of amiodarone, nifekalant, dofetilide, d/l-sotalol, flecainide, and moxifloxacin. We selected these drugs based on their different arrhythmogenic potentials and abilities to facilitate hERG current. Our docking studies and clustering provided atomistic structural insights into state-dependent drug-channel interactions that play a key role in differentiating safe and harmful hERG blockers and can explain hERG channel facilitation through drug interactions with its open-state hydrophobic pockets.

Role of environmental specificity in CASP results

BACKGROUND

Recently, significant progress has been made in the field of protein structure prediction by the application of artificial intelligence techniques, as shown by the results of the CASP13 and CASP14 (Critical Assessment of Structure Prediction) competition. However, the question of the mechanism behind the protein folding process itself remains unanswered. Correctly predicting the structure also does not solve the problem of, for example, amyloid proteins, where a polypeptide chain with an unaltered sequence adopts a different 3D structure.

RESULTS

This work was an attempt at explaining the structural variation by considering the contribution of the environment to protein structuring. The application of the fuzzy oil drop (FOD) model to assess the validity of the selected models provided in the CASP13, CASP14 and CASP15 projects reveals the need for an environmental factor to determine the 3D structure of proteins. Consideration of the external force field in the form of polar water (Fuzzy Oil Drop) and a version modified by the presence of the hydrophobic compounds, FOD-M (FOD-Modified) reveals that the protein folding process is environmentally dependent. An analysis of selected models from the CASP competitions indicates the need for structure prediction as dependent on the consideration of the protein folding environment.

CONCLUSIONS

The conditions governed by the environment direct the protein folding process occurring in a certain environment. Therefore, the variation of the external force field should be taken into account in the models used in protein structure prediction.

Coarse-grained potential for hydrogen bond interactions

Understanding protein structure and dynamics is crucial for investigating numerous biological processes. This however requires proper description of molecular interactions, most notably hydrogen bonds, which are the driving force behind the folding of protein sequences into working molecules. Due to the multi-body character of this interaction, proper mathematical formulation has been a matter of long debate in the literature. This description becomes even more complex in reduced protein models. In this contribution, we propose a novel hydrogen bond energy function definition that is based only on Cα positions and used for coarse-grained simulations. We show that this new method has the capability to recognize hydrogen bonds with over 80% accuracy and can successfully identify β-sheet in β-amyloid peptide simulations.

Computational design of novel Cas9 PAM-interacting domains using evolution-based modelling and structural quality assessment

We present here an approach to protein design that combines (i) scarce functional information such as experimental data (ii) evolutionary information learned from a natural sequence variants and (iii) physics-grounded modeling. Using a Restricted Boltzmann Machine (RBM), we learn a sequence model of a protein family. We use semi-supervision to leverage available functional information during the RBM training. We then propose a strategy to explore the protein representation space that can be informed by external models such as an empirical force-field method (FoldX). Our approach is applied to a domain of the Cas9 protein responsible for recognition of a short DNA motif. We experimentally assess the functionality of 71 variants generated to explore a range of RBM and FoldX energies. Sequences with as many as 50 differences (20% of the protein domain) to the wild-type retained functionality. Overall, 21/71 sequences designed with our method were functional. Interestingly, 6/71 sequences showed an improved activity in comparison with the original wild-type protein sequence. These results demonstrate the interest in further exploring the synergies between machine-learning of protein sequence representations and physics grounded modeling strategies informed by structural information.

PROSTATA: a framework for protein stability assessment using transformers

MOTIVATION

Accurate prediction of change in protein stability due to point mutations is an attractive goal that remains unachieved. Despite the high interest in this area, little consideration has been given to the transformer architecture, which is dominant in many fields of machine learning.

RESULTS

In this work, we introduce PROSTATA, a predictive model built in a knowledge-transfer fashion on a new curated dataset. PROSTATA demonstrates advantage over existing solutions based on neural networks. We show that the large improvement margin is due to both the architecture of the model and the quality of the new training dataset. This work opens up opportunities to develop new lightweight and accurate models for protein stability assessment.

AVAILABILITY AND IMPLEMENTATION

PROSTATA is available at https://github.com/AIRI-Institute/PROSTATA and https://prostata.airi.net.

Dynamics simulation, energetics calculation and experimental analysis of the intermolecular interaction between human neonatal ABL SH3 domain and its N-substituted peptoid ligands

Non-receptor tyrosine kinase of neonatal ABL (nABL) is distributed in the nucleus and cytoplasm of proliferating cells in embryo and neonate, and has been implicated in the pathogenesis of neonatal leukemia and other hematological diseases. The kinase contains a regulatory Src homology 3 (SH3) domain that can specifically recognize proline-rich peptide segments on its partner protein surface. In this study, we systematically investigated the N-substitution effect on the binding of an empirically designed proline-rich peptide p9 to nABL SH3 domain by integrating dynamics simulations, energetics calculations and fluorescence affinity assays. The p9 is an almost all proline-composed decapeptide, with only a sole tyrosine at its residue 4, which has been found to bind nABL SH3 domain at a micromolar level in a class I mode. Here, the non-key residues of p9 peptide were independently replaced by various N-substituted amino acids to create a systematic N-substitution profile, from which we can identify those favorable, neutral and unfavorable substitutions at each peptide residue. On this basis a combinatorial peptoid library was rationally designed by systematically combining the favorable N-substituted amino acids at non-key residues of p9 peptide, thus resulting in a number of its peptoid counterparts. The binding affinity of top peptoid hits was observed to be comparable with or improved moderately relative to p9 peptide, with Kd ranging between 3.1 and 76 μM. Structural analysis revealed that the peptoids can be divided into exposed, polar and hydrophobic regions from N- to C-termini, in which the polar and hydrophobic regions confer specificity and stability to the domain-peptoid interaction, respectively. In addition, a designed peptoid was also observed to exhibit 5.3-fold SH3-selectivity for nABL over cSRC, suggesting that the N-substitution can be used to improve not only binding affinity but also recognition specificity of SH3 binders.Communicated by Ramaswamy H. Sarma.

UCSF ChimeraX: Tools for structure building and analysis

Advances in computational tools for atomic model building are leading to accurate models of large molecular assemblies seen in electron microscopy, often at challenging resolutions of 3-4 Å. We describe new methods in the UCSF ChimeraX molecular modeling package that take advantage of machine-learning structure predictions, provide likelihood-based fitting in maps, and compute per-residue scores to identify modeling errors. Additional model-building tools assist analysis of mutations, post-translational modifications, and interactions with ligands. We present the latest ChimeraX model-building capabilities, including several community-developed extensions. ChimeraX is available free of charge for noncommercial use at https://www.rbvi.ucsf.edu/chimerax.

Predictive Modeling and Structure Analysis of Genetic Variants in Familial Hypercholesterolemia: Implications for Diagnosis and Protein Interaction Studies

PURPOSE OF REVIEW

Familial hypercholesterolemia (FH) is a hereditary condition characterized by elevated levels of low-density lipoprotein cholesterol (LDL-C), which increases the risk of cardiovascular disease if left untreated. This review aims to discuss the role of bioinformatics tools in evaluating the pathogenicity of missense variants associated with FH. Specifically, it highlights the use of predictive models based on protein sequence, structure, evolutionary conservation, and other relevant features in identifying genetic variants within LDLR, APOB, and PCSK9 genes that contribute to FH.

RECENT FINDINGS

In recent years, various bioinformatics tools have emerged as valuable resources for analyzing missense variants in FH-related genes. Tools such as REVEL, Varity, and CADD use diverse computational approaches to predict the impact of genetic variants on protein function. These tools consider factors such as sequence conservation, structural alterations, and receptor binding to aid in interpreting the pathogenicity of identified missense variants. While these predictive models offer valuable insights, the accuracy of predictions can vary, especially for proteins with unique characteristics that might not be well represented in the databases used for training. This review emphasizes the significance of utilizing bioinformatics tools for assessing the pathogenicity of FH-associated missense variants. Despite their contributions, a definitive diagnosis of a genetic variant necessitates functional validation through in vitro characterization or cascade screening. This step ensures the precise identification of FH-related variants, leading to more accurate diagnoses. Integrating genetic data with reliable bioinformatics predictions and functional validation can enhance our understanding of the genetic basis of FH, enabling improved diagnosis, risk stratification, and personalized treatment for affected individuals. The comprehensive approach outlined in this review promises to advance the management of this inherited disorder, potentially leading to better health outcomes for those affected by FH.

Peptide binder design with inverse folding and protein structure prediction

The computational design of peptide binders towards a specific protein interface can aid diagnostic and therapeutic efforts. Here, we design peptide binders by combining the known structural space searched with Foldseek, the protein design method ESM-IF1, and AlphaFold2 (AF) in a joint framework. Foldseek generates backbone seeds for a modified version of ESM-IF1 adapted to protein complexes. The resulting sequences are evaluated with AF using an MSA representation for the receptor structure and a single sequence for the binder. We show that AF can accurately evaluate protein binders and that our bind score can select these (ROC AUC = 0.96 for the heterodimeric case). We find that designs created from seeds with more contacts per residue are more successful and tend to be short. There is a relationship between the sequence recovery in interface positions and the plDDT of the designs, where designs with ≥80% recovery have an average plDDT of 84 compared to 55 at 0%. Designed sequences have 60% higher median plDDT values towards intended receptors than non-intended ones. Successful binders (predicted interface RMSD ≤ 2 Å) are designed towards 185 (6.5%) heteromeric and 42 (3.6%) homomeric protein interfaces with ESM-IF1 compared with 18 (1.5%) using ProteinMPNN from 100 samples.

Comprehensive Screening of a Light-Inducible Split Cre Recombinase with Domain Insertion Profiling

Splitting proteins with light- or chemically inducible dimers provides a mechanism for post-translational control of protein function. However, current methods for engineering stimulus-responsive split proteins often require significant protein engineering expertise and the laborious screening of individual constructs. To address this challenge, we use a pooled library approach that enables rapid generation and screening of nearly all possible split protein constructs in parallel, where results can be read out by using sequencing. We perform our method on Cre recombinase with optogenetic dimers as a proof of concept, resulting in comprehensive data on the split sites throughout the protein. To improve the accuracy in predicting split protein behavior, we develop a Bayesian computational approach to contextualize errors inherent to experimental procedures. Overall, our method provides a streamlined approach for achieving inducible post-translational control of a protein of interest.

Enzymatic polyethylene biorecycling: Confronting challenges and shaping the future

Polyethylene (PE) is a widely used plastic known for its resistance to biodegradation, posing a significant environmental challenge. Recent advances have shed light on microorganisms and insects capable of breaking down PE and identified potential PE-degrading enzymes (PEases), hinting at the possibility of PE biorecycling. Research on enzymatic PE degradation is still in its early stages, especially compared to the progress made with polyethylene terephthalate (PET). While PET hydrolases have been extensively studied and engineered for improved performance, even the products of PEases remain mostly undefined. This Perspective analyzes the current state of enzymatic PE degradation research, highlighting obstacles in the search for bona fide PEases and suggesting areas for future exploration. A critical challenge impeding progress in this field stems from the inert nature of the C-C and C-H bonds of PE. Furthermore, breaking down PE into small molecules using only one monofunctional enzyme is theoretically impossible. Overcoming these obstacles requires identifying enzymatic pathways, which can be facilitated using emerging technologies like omics, structure-based design, and computer-assisted engineering of enzymes. Understanding the mechanisms underlying PE enzymatic biodegradation is crucial for research progress and for identifying potential solutions to the global plastic pollution crisis.

Awakening the natural capability of psicose production in Escherichia coli

Due to the rampant rise in obesity and diabetes, consumers are desperately seeking for ways to reduce their sugar intake, but to date there are no options that are both accessible and without sacrifice of palatability. One of the most promising new ingredients in the food system as a non-nutritive sugar substitute with near perfect palatability is D-psicose. D-psicose is currently produced using an in vitro enzymatic isomerization of D-fructose, resulting in low yield and purity, and therefore requiring substantial downstream processing to obtain a high purity product. This has made adoption of D-psicose into products limited and results in significantly higher per unit costs, reducing accessibility to those most in need. Here, we found that Escherichia coli natively possesses a thermodynamically favorable pathway to produce D-psicose from D-glucose through a series of phosphorylation-epimerization-dephosphorylation steps. To increase carbon flux towards D-psicose production, we introduced a series of genetic modifications to pathway enzymes, central carbon metabolism, and competing metabolic pathways. In an attempt to maximize both cellular viability and D-psicose production, we implemented methods for the dynamic regulation of key genes including clustered regularly interspaced short palindromic repeats inhibition (CRISPRi) and stationary-phase promoters. The engineered strains achieved complete consumption of D-glucose and production of D-psicose, at a titer of 15.3 g L-1, productivity of 2 g L-1 h-1, and yield of 62% under test tube conditions. These results demonstrate the viability of whole-cell catalysis as a sustainable alternative to in vitro enzymatic synthesis for the accessible production of D-psicose.

Homologous Pairs of Low and High Temperature Originating Proteins Spanning the Known Prokaryotic Universe

Stability of proteins at high temperature has been a topic of interest for many years, as this attribute is favourable for applications ranging from therapeutics to industrial chemical manufacturing. Our current understanding and methods for designing high-temperature stability into target proteins are inadequate. To drive innovation in this space, we have curated a large dataset, learn2thermDB, of protein-temperature examples, totalling 24 million instances, and paired proteins across temperatures based on homology, yielding 69 million protein pairs - orders of magnitude larger than the current largest. This important step of pairing allows for study of high-temperature stability in a sequence-dependent manner in the big data era. The data pipeline is parameterized and open, allowing it to be tuned by downstream users. We further show that the data contains signal for deep learning. This data offers a new doorway towards thermal stability design models.