Neutralization and Stability of SARS-CoV-2 Omicron Variant

AI-based structure prediction empowers integrative structural analysis of human nuclear pores

INTRODUCTION The eukaryotic nucleus pro-tects the genome and is enclosed by the two membranes of the nuclear envelope. Nuclear pore complexes (NPCs) perforate the nuclear envelope to facilitate nucleocytoplasmic transport. With a molecular weight of ∼120 MDa, the human NPC is one of the larg-est protein complexes. Its ~1000 proteins are taken in multiple copies from a set of about 30 distinct nucleoporins (NUPs). They can be roughly categorized into two classes. Scaf-fold NUPs contain folded domains and form a cylindrical scaffold architecture around a central channel. Intrinsically disordered NUPs line the scaffold and extend into the central channel, where they interact with cargo complexes. The NPC architecture is highly dynamic. It responds to changes in nuclear envelope tension with conforma-tional breathing that manifests in dilation and constriction movements. Elucidating the scaffold architecture, ultimately at atomic resolution, will be important for gaining a more precise understanding of NPC function and dynamics but imposes a substantial chal-lenge for structural biologists. RATIONALE Considerable progress has been made toward this goal by a joint effort in the field. A synergistic combination of complementary approaches has turned out to be critical. In situ structural biology techniques were used to reveal the overall layout of the NPC scaffold that defines the spatial reference for molecular modeling. High-resolution structures of many NUPs were determined in vitro. Proteomic analysis and extensive biochemical work unraveled the interaction network of NUPs. Integra-tive modeling has been used to combine the different types of data, resulting in a rough outline of the NPC scaffold. Previous struc-tural models of the human NPC, however, were patchy and limited in accuracy owing to several challenges: (i) Many of the high-resolution structures of individual NUPs have been solved from distantly related species and, consequently, do not comprehensively cover their human counterparts. (ii) The scaf-fold is interconnected by a set of intrinsically disordered linker NUPs that are not straight-forwardly accessible to common structural biology techniques. (iii) The NPC scaffold intimately embraces the fused inner and outer nuclear membranes in a distinctive topol-ogy and cannot be studied in isolation. (iv) The conformational dynamics of scaffold NUPs limits the resolution achievable in structure determination. RESULTS In this study, we used artificial intelligence (AI)-based prediction to generate an exten-sive repertoire of structural models of human NUPs and their subcomplexes. The resulting models cover various domains and interfaces that so far remained structurally uncharac-terized. Benchmarking against previous and unpublished x-ray and cryo-electron micros-copy structures revealed unprecedented accu-racy. We obtained well-resolved cryo-electron tomographic maps of both the constricted and dilated conformational states of the hu-man NPC. Using integrative modeling, we fit-ted the structural models of individual NUPs into the cryo-electron microscopy maps. We explicitly included several linker NUPs and traced their trajectory through the NPC scaf-fold. We elucidated in great detail how mem-brane-associated and transmembrane NUPs are distributed across the fusion topology of both nuclear membranes. The resulting architectural model increases the structural coverage of the human NPC scaffold by about twofold. We extensively validated our model against both earlier and new experimental data. The completeness of our model has enabled microsecond-long coarse-grained molecular dynamics simulations of the NPC scaffold within an explicit membrane en-vironment and solvent. These simulations reveal that the NPC scaffold prevents the constriction of the otherwise stable double-membrane fusion pore to small diameters in the absence of membrane tension. CONCLUSION Our 70-MDa atomically re-solved model covers >90% of the human NPC scaffold. It captures conforma-tional changes that occur during dilation and constriction. It also reveals the precise anchoring sites for intrinsically disordered NUPs, the identification of which is a prerequisite for a complete and dy-namic model of the NPC. Our study exempli-fies how AI-based structure prediction may accelerate the elucidation of subcellular ar-chitecture at atomic resolution. [Figure: see text].

The Iron Deficiency-Regulated Small Protein Effector FEP3/IRON MAN1 Modulates Interaction of BRUTUS-LIKE1 With bHLH Subgroup IVc and POPEYE Transcription Factors

In light of climate change and human population growth one of the most challenging tasks is to generate plants that are Fe-efficient, resilient to low Fe supply and Fe-biofortified. For such endeavors, it is crucial to understand the regulation of Fe acquisition and allocation in plants. One open question is how identified Fe-regulatory proteins comprising positive and negative regulators act together to steer Fe homeostasis. bHLH transcription factors (TFs) belonging to the subgroups IVb and IVc can initiate a bHLH cascade controlling the -Fe response in roots. In Arabidopsis thaliana, the -Fe-induced genes are sub-divided into several gene co-expression clusters controlled by different sets of TFs. Some of the co-expressed genes encode regulatory E3 ligase proteins BRUTUS (BTS)/BTS-LIKE (BTSL) and small proteins belonging to the group of FE UPTAKE-INDUCING PEPTIDE/IRON MAN (FEP/IMA). Recently, it was described that FEP1/IMA3 and FEP3/IMA1 proteins inhibit the repression of bHLH factors by BTS. We had postulated that -Fe-regulated co-expression clusters provide new information about regulatory protein interaction complexes. Here, we report a targeted yeast two-hybrid screen among 23 proteins of the -Fe response. This identified a novel protein interactome involving another E3 ligase, namely BTSL1, basic helix-loop-helix (bHLH) protein POPEYE (PYE) and transcription factors of the subgroup IVc as well as FEP3/IMA1. Because of the difficulty in stable BTSL1 protein expression in plant cells, we used a yeast two hybrid-based deletion mapping, homology modeling and molecular docking, to pinpoint interaction sites in BTSL1 and FEP3/IMA1. bHLH IVc TFs have similar residues at their C-terminus as FEP3/IMA1 interacting sites. FEP3/IMA1 attenuated interaction of BTSL1 and bHLH proteins in a yeast three-hybrid assay, in line with physiological data pointing to enhanced Fe acquisition and allocation in FEP3/IMA1 overexpression and btsl1 btsl2 mutant plants. Hence, exploiting -Fe-induced gene co-expression networks identified FEP3/IMA1 as a small effector protein that binds and inhibits the BTSL1 complex with PYE and bHLH subgroup IVc proteins. Structural analysis resolved interaction sites. This information helps improving models of Fe regulation and identifying novel targets for breeding of Fe-efficient crops.

The structure of EXTL3 helps to explain the different roles of bi-domain exostosins in heparan sulfate synthesis

Heparan sulfate is a highly modified O-linked glycan that performs diverse physiological roles in animal tissues. Though quickly modified, it is initially synthesised as a polysaccharide of alternating β-D-glucuronosyl and N-acetyl-α-D-glucosaminyl residues by exostosins. These enzymes generally possess two glycosyltransferase domains (GT47 and GT64)-each thought to add one type of monosaccharide unit to the backbone. Although previous structures of murine exostosin-like 2 (EXTL2) provide insight into the GT64 domain, the rest of the bi-domain architecture is yet to be characterised; hence, how the two domains co-operate is unknown. Here, we report the structure of human exostosin-like 3 (EXTL3) in apo and UDP-bound forms. We explain the ineffectiveness of EXTL3's GT47 domain to transfer β-D-glucuronosyl units, and we observe that, in general, the bi-domain architecture would preclude a processive mechanism of backbone extension. We therefore propose that heparan sulfate backbone polymerisation occurs by a simple dissociative mechanism.

Dynamic interplay between the periplasmic chaperone SurA and the BAM complex in outer membrane protein folding

Correct folding of outer membrane proteins (OMPs) into the outer membrane of Gram-negative bacteria depends on delivery of unfolded OMPs to the β-barrel assembly machinery (BAM). How unfolded substrates are presented to BAM remains elusive, but the major OMP chaperone SurA is proposed to play a key role. Here, we have used hydrogen deuterium exchange mass spectrometry (HDX-MS), crosslinking, in vitro folding and binding assays and computational modelling to show that the core domain of SurA and one of its two PPIase domains are key to the SurA-BAM interaction and are required for maximal catalysis of OMP folding. We reveal that binding causes changes in BAM and SurA conformation and/or dynamics distal to the sites of binding, including at the BamA β1-β16 seam. We propose a model for OMP biogenesis in which SurA plays a crucial role in OMP delivery and primes BAM to accept substrates for folding.

Interaction of the outer membrane protein (OMP) chaperone SurA and the OMP folding catalyst BAM results in changes in the conformational ensembles of both species, suggesting a mechanism for delivery of OMPs to BAM in Gram-negative bacteria.

ColabFold: making protein folding accessible to all

ColabFold offers accelerated prediction of protein structures and complexes by combining the fast homology search of MMseqs2 with AlphaFold2 or RoseTTAFold. ColabFold's 40-60-fold faster search and optimized model utilization enables prediction of close to 1,000 structures per day on a server with one graphics processing unit. Coupled with Google Colaboratory, ColabFold becomes a free and accessible platform for protein folding. ColabFold is open-source software available at https://github.com/sokrypton/ColabFold and its novel environmental databases are available at https://colabfold.mmseqs.com .

From systems to structure - using genetic data to model protein structures

Understanding the effects of genetic variation is a fundamental problem in biology that requires methods to analyse both physical and functional consequences of sequence changes at systems-wide and mechanistic scales. To achieve a systems view, protein interaction networks map which proteins physically interact, while genetic interaction networks inform on the phenotypic consequences of perturbing these protein interactions. Until recently, understanding the molecular mechanisms that underlie these interactions often required biophysical methods to determine the structures of the proteins involved. The past decade has seen the emergence of new approaches based on coevolution, deep mutational scanning and genome-scale genetic or chemical-genetic interaction mapping that enable modelling of the structures of individual proteins or protein complexes. Here, we review the emerging use of large-scale genetic datasets and deep learning approaches to model protein structures and their interactions, and discuss the integration of structural data from different sources.

Artificial intelligence-assisted cryoEM structure of Bfr2-Lcp5 complex observed in the yeast small subunit processome

Eukaryotic ribosome is maturated through an elaborate process that includes modification, processing and folding of pre-ribosomal RNA (pre-rRNAs) by a series of ribosome assembly intermediates. More than 70 factors participate in the dynamic assembly and disassembly of the small subunit processome (90S) inside nucleolus, leading to the early maturation of small subunit. The 5' domain of the 18S rRNA is the last to be incorporated into the stable 90S prior to the cleavage of pre-rRNA at the A1 site. This step is facilitated by the Kre33-Enp2-Bfr2-Lcp5 protein module with the participation of the DEAD-box protein Dbp4. Though structures of Kre33 and Enp2 have been modeled in previously observed 90S structures, that of Bfr2-Lcp5 complex remains unavailable. Here, we report an AlphaFold-assisted structure determination of the Bfr2-Lcp5 complex captured in a 3.99 Å - 7.24 Å cryoEM structure of 90S isolated from yeast cells depleted of Pih1, a chaperone protein of the 90S core assembly. The structure model is consistent with the protein-protein interaction results and the secondary structures of recombinant Bfr2 and Bfr2-Lcp5 complex obtained by Circular Dichroism. The Bfr2-Lcp5 complex interaction mimics that of exosome factors Rrp6-Rrp47 and acts to regulate 90S transitions.

Brachypodium Antifreeze Protein Gene Products Inhibit Ice Recrystallisation, Attenuate Ice Nucleation, and Reduce Immune Response

Antifreeze proteins (AFPs) from the model crop, Brachypodium distachyon, allow freeze survival and attenuate pathogen-mediated ice nucleation. Intriguingly, Brachypodium AFP genes encode two proteins, an autonomous AFP and a leucine-rich repeat (LRR). We present structural models which indicate that ice-binding motifs on the ~13 kDa AFPs can "spoil" nucleating arrays on the ~120 kDa bacterial ice nucleating proteins used to form ice at high sub-zero temperatures. These models are consistent with the experimentally demonstrated decreases in ice nucleating activity by lysates from wildtype compared to transgenic Brachypodium lines. Additionally, the expression of Brachypodium LRRs in transgenic Arabidopsis inhibited an immune response to pathogen flagella peptides (flg22). Structural models suggested that this was due to the affinity of the LRR domains to flg22. Overall, it is remarkable that the Brachypodium genes play multiple distinctive roles in connecting freeze survival and anti-pathogenic systems via their encoded proteins' ability to adsorb to ice as well as to attenuate bacterial ice nucleation and the host immune response.

Delineating the conformational landscape and intrinsic properties of the angiotensin II type 2 receptor using a computational study

As a key regulator for the renin-angiotensin system, a class A G protein-coupled receptor (GPCR), AngII type 2 receptor (AT₂R), plays a pivotal role in the homeostasis of the cardiovascular system. Compared with other GPCRs, AT₂R has a unique antagonist-bound conformation and its mechanism is still an enigma. Here, we applied combined dynamic and evolutional approaches to investigate the conformational space and intrinsic properties of AT₂R. With molecular dynamic simulations, Markov State Models, and statistics coupled analysis, we captured the conformational landscape of AT₂R and identified its uniquity from both dynamical and evolutional viewpoints. A cryptic pocket was also discovered in the intermediate state during conformation transitions. These findings offer a deeper understanding of the AT₂R mechanism at an atomic level and provide hints for the design of novel AT₂R modulators.

PepNN: a deep attention model for the identification of peptide binding sites

Protein-peptide interactions play a fundamental role in many cellular processes, but remain underexplored experimentally and difficult to model computationally. Here, we present PepNN-Struct and PepNN-Seq, structure and sequence-based approaches for the prediction of peptide binding sites on a protein. A main difficulty for the prediction of peptide-protein interactions is the flexibility of peptides and their tendency to undergo conformational changes upon binding. Motivated by this, we developed reciprocal attention to simultaneously update the encodings of peptide and protein residues while enforcing symmetry, allowing for information flow between the two inputs. PepNN integrates this module with modern graph neural network layers and a series of transfer learning steps are used during training to compensate for the scarcity of peptide-protein complex information. We show that PepNN-Struct achieves consistently high performance across different benchmark datasets. We also show that PepNN makes reasonable peptide-agnostic predictions, allowing for the identification of novel peptide binding proteins.

Substrate Specificity of the Flavoenzyme BhaC1 That Converts a C-Terminal Trp to a Hydroxyquinone

The preparation of protein-protein, protein-peptide, and protein-small molecule conjugates is important for a variety of applications, such as vaccine production, immunotherapies, preparation of antibody-drug conjugates, and targeted delivery of therapeutics. To achieve site-selective conjugation, selective chemical or enzymatic functionalization of proteins is required. We have recently reported biosynthetic pathways in which small, catalytic scaffold peptides are utilized for the generation of amino acid-derived natural products called pearlins. In these systems, peptide amino-acyl tRNA ligases (PEARLs) append amino acids to the C-terminus of a scaffold peptide, and tailoring enzymes encoded in the biosynthetic gene clusters modify the PEARL-appended amino acid to generate a variety of natural products. Herein, we investigate the substrate selectivity of one such tailoring enzyme, BhaC₁, that participates in pyrroloiminoquinone biosynthesis. BhaC₁ converts the indole of a C-terminal tryptophan into an o-hydroxy-p-quinone, a promising moiety for site-selective bioconjugation. Our studies demonstrate that BhaC₁ requires a 20-amino acid peptide for substrate recognition. When this peptide was appended at the C-terminus of proteins, the C-terminal Trp was modified by BhaC₁. The enzyme is sufficiently selective that only small changes to the sequence of the peptide are tolerated. An AlphaFold model for substrate recognition explains the selectivity of the enzyme, which may be used to install a reactive handle onto the C-terminus of proteins.

Large-Scale Discovery of Microbial Fibrillar Adhesins and Identification of Novel Members of Adhesive Domain Families

Fibrillar adhesins are bacterial cell surface proteins that mediate interactions with the environment, including host cells during colonization or other bacteria during biofilm formation. These proteins are characterized by a stalk that projects the adhesive domain closer to the binding target. Fibrillar adhesins evolve quickly and thus can be difficult to computationally identify, yet they represent an important component for understanding bacterium-host interactions. To detect novel fibrillar adhesins, we developed a random forest prediction approach based on common characteristics we identified for this protein class. We applied this approach to Firmicutes and Actinobacteria proteomes, yielding over 6,500 confidently predicted fibrillar adhesins. To verify the approach, we investigated predicted fibrillar adhesins that lacked a known adhesive domain. Based on these proteins, we identified 24 sequence clusters representing potential novel members of adhesive domain families. We used AlphaFold to verify that 15 clusters showed structural similarity to known adhesive domains, such as the TED domain. Overall, our study has made a significant contribution to the number of known fibrillar adhesins and has enabled us to identify novel members of adhesive domain families involved in bacterial pathogenesis.

IMPORTANCE Fibrillar adhesins are a class of bacterial cell surface proteins that enable bacteria to interact with their environment. We developed a machine learning approach to identify fibrillar adhesins and applied this classification approach to the Firmicutes and Actinobacteria Reference Proteomes database. This method allowed us to detect a high number of novel fibrillar adhesins and also novel members of adhesive domain families. To confirm our predictions of these potential adhesin protein domains, we predicted their structure using the AlphaFold tool.

CSM-Potential: mapping protein interactions and biological ligands in 3D space using geometric deep learning

Recent advances in protein structural modelling have enabled the accurate prediction of the holo 3D structures of almost any protein, however protein function is intrinsically linked to the interactions it makes. While a number of computational approaches have been proposed to explore potential biological interactions, they have been limited to specific interactions, and have not been readily accessible for non-experts or use in bioinformatics pipelines. Here we present CSM-Potential, a geometric deep learning approach to identify regions of a protein surface that are likely to mediate protein-protein and protein-ligand interactions in order to provide a link between 3D structure and biological function. Our method has shown robust performance, outperforming existing methods for both predictive tasks. By assessing the performance of CSM-Potential on independent blind tests, we show that our method was able to achieve ROC AUC values of up to 0.81 for the identification of potential protein-protein binding sites, and up to 0.96 accuracy on biological ligand classification. Our method is freely available as a user-friendly and easy-to-use web server and API at http://biosig.unimelb.edu.au/csm_potential.

The Specific Capsule Depolymerase of Phage PMK34 Sensitizes Acinetobacter baumannii to Serum Killing

The rising antimicrobial resistance is particularly alarming for Acinetobacter baumannii, calling for the discovery and evaluation of alternatives to treat A. baumannii infections. Some bacteriophages produce a structural protein that depolymerizes capsular exopolysaccharide. Such purified depolymerases are considered as novel antivirulence compounds. We identified and characterized a depolymerase (DpoMK34) from Acinetobacter phage vB_AbaP_PMK34 active against the clinical isolate A. baumannii MK34. In silico analysis reveals a modular protein displaying a conserved N-terminal domain for anchoring to the phage tail, and variable central and C-terminal domains for enzymatic activity and specificity. AlphaFold-Multimer predicts a trimeric protein adopting an elongated structure due to a long α-helix, an enzymatic β-helix domain and a hypervariable 4 amino acid hotspot in the most ultimate loop of the C-terminal domain. In contrast to the tail fiber of phage T3, this hypervariable hotspot appears unrelated with the primary receptor. The functional characterization of DpoMK34 revealed a mesophilic enzyme active up to 50 °C across a wide pH range (4 to 11) and specific for the capsule of A. baumannii MK34. Enzymatic degradation of the A. baumannii MK34 capsule causes a significant drop in phage adsorption from 95% to 9% after 5 min. Although lacking intrinsic antibacterial activity, DpoMK34 renders A. baumannii MK34 fully susceptible to serum killing in a serum concentration dependent manner. Unlike phage PMK34, DpoMK34 does not easily select for resistant mutants either against PMK34 or itself. In sum, DpoMK34 is a potential antivirulence compound that can be included in a depolymerase cocktail to control difficult to treat A. baumannii infections.

Systems Biology of Virus-Host Protein Interactions: From Hypothesis Generation to Mechanisms of Replication and Pathogenesis

As obligate intracellular parasites, all viruses must co-opt cellular machinery to facilitate their own replication. Viruses often co-opt these cellular pathways and processes through physical interactions between viral and host proteins. In addition to facilitating fundamental aspects of virus replication cycles, these virus-host protein interactions can also disrupt physiological functions of host proteins, causing disease that can be advantageous to the virus or simply a coincidence. Consequently, unraveling virus-host protein interactions can serve as a window into molecular mechanisms of virus replication and pathogenesis. Identifying virus-host protein interactions using unbiased systems biology approaches provides an avenue for hypothesis generation. This review highlights common systems biology approaches for identification of virus-host protein interactions and the mechanistic insights revealed by these methods. We also review conceptual innovations using comparative and integrative systems biology that can leverage global virus-host protein interaction data sets to more rapidly move from hypothesis generation to mechanism. Expected final online publication date for the Annual Review of Virology, Volume 9 is September 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

SDR enzymes oxidize specific lipidic alkynylcarbinols into cytotoxic protein-reactive species

Hundreds of cytotoxic natural or synthetic lipidic compounds contain chiral alkynylcarbinol motifs, but the mechanism of action of those potential therapeutic agents remains unknown. Using a genetic screen in haploid human cells, we discovered that the enantiospecific cytotoxicity of numerous terminal alkynylcarbinols, including the highly cytotoxic dialkynylcarbinols, involves a bioactivation by HSD17B11, a short-chain dehydrogenase/reductase (SDR) known to oxidize the C-17 carbinol center of androstan-3-alpha,17-beta-diol to the corresponding ketone. A similar oxidation of dialkynylcarbinols generates dialkynylketones, that we characterize as highly protein-reactive electrophiles. We established that, once bioactivated in cells, the dialkynylcarbinols covalently modify several proteins involved in protein-quality control mechanisms, resulting in their lipoxidation on cysteines and lysines through Michael addition. For some proteins, this triggers their association to cellular membranes and results in endoplasmic reticulum stress, unfolded protein response activation, ubiquitin-proteasome system inhibition and cell death by apoptosis. Finally, as a proof-of-concept, we show that generic lipidic alkynylcarbinols can be devised to be bioactivated by other SDRs, including human RDH11 and HPGD/15-PGDH. Given that the SDR superfamily is one of the largest and most ubiquitous, this unique cytotoxic mechanism-of-action could be widely exploited to treat diseases, in particular cancer, through the design of tailored prodrugs.

Molecular insights into RNA recognition and gene regulation by the TRIM-NHL protein Mei-P26

The TRIM-NHL protein Meiotic P26 (Mei-P26) acts as a regulator of cell fate in Drosophila Its activity is critical for ovarian germline stem cell maintenance, differentiation of oocytes, and spermatogenesis. Mei-P26 functions as a post-transcriptional regulator of gene expression; however, the molecular details of how its NHL domain selectively recognizes and regulates its mRNA targets have remained elusive. Here, we present the crystal structure of the Mei-P26 NHL domain at 1.6 Å resolution and identify key amino acids that confer substrate specificity and distinguish Mei-P26 from closely related TRIM-NHL proteins. Furthermore, we identify mRNA targets of Mei-P26 in cultured Drosophila cells and show that Mei-P26 can act as either a repressor or activator of gene expression on different RNA targets. Our work reveals the molecular basis of RNA recognition by Mei-P26 and the fundamental functional differences between otherwise very similar TRIM-NHL proteins.

Protein-protein and protein-lipid interactions of pore-forming BCL-2 family proteins in apoptosis initiation

Apoptosis is a common cell death program that is important in human health and disease. Signaling in apoptosis is largely driven through protein-protein interactions. The BCL-2 family proteins function in protein-protein interactions as key regulators of mitochondrial poration, the process that initiates apoptosis through the release of cytochrome c, which activates the apoptotic caspase cascade leading to cellular demolition. The BCL-2 pore-forming proteins BAK and BAX are the key executors of mitochondrial poration. We review the state of knowledge of protein-protein and protein-lipid interactions governing the apoptotic function of BAK and BAX, as determined through X-ray crystallography and NMR spectroscopy studies. BAK and BAX are dormant, globular α-helical proteins that participate in protein-protein interactions with other pro-death BCL-2 family proteins, transforming them into active, partially unfolded proteins that dimerize and associate with and permeabilize mitochondrial membranes. We compare the protein-protein interactions observed in high-resolution structures with those derived in silico by AlphaFold, making predictions based on combining experimental and in silico approaches to delineate the structural basis for novel protein-protein interaction complexes of BCL-2 family proteins.

Integration of Protein Structure and Population-Scale DNA Sequence Data for Disease Gene Discovery and Variant Interpretation

The experimental and computational techniques for capturing information about protein structures and genetic variation within the human genome have advanced dramatically in the past 20 years, generating extensive new data resources. In this review, we discuss these advances, along with new approaches for determining the impact a genetic variant has on protein function. We focus on the potential of new methods that integrate human genetic variation into protein structures to discover relationships to disease, including the discovery of mutational hotspots in cancer-related proteins, the localization of protein-altering variants within protein regions for common complex diseases, and the assessment of variants of unknown significance for Mendelian traits. We expect that approaches that integrate these data sources will play increasingly important roles in disease gene discovery and variant interpretation. Expected final online publication date for the Annual Review of Biomedical Data Science, Volume 5 is August 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Structure of the RZZ complex and molecular basis of Spindly-driven corona assembly at human kinetochores

In metazoans, a ≈1 megadalton (MDa) multiprotein complex comprising the dynein-dynactin adaptor Spindly and the ROD-Zwilch-ZW10 (RZZ) complex is the building block of a fibrous biopolymer, the kinetochore fibrous corona. The corona assembles on mitotic kinetochores to promote microtubule capture and spindle assembly checkpoint (SAC) signaling. We report here a high-resolution cryo-EM structure that captures the essential features of the RZZ complex, including a farnesyl-binding site required for Spindly binding. Using a highly predictive in vitro assay, we demonstrate that the SAC kinase MPS1 is necessary and sufficient for corona assembly at supercritical concentrations of the RZZ-Spindly (RZZS) complex, and describe the molecular mechanism of phosphorylation-dependent filament nucleation. We identify several structural requirements for RZZS polymerization in rings and sheets. Finally, we identify determinants of kinetochore localization and corona assembly of Spindly. Our results describe a framework for the long-sought-for molecular basis of corona assembly on metazoan kinetochores.

The structural context of posttranslational modifications at a proteome-wide scale

The recent revolution in computational protein structure prediction provides folding models for entire proteomes, which can now be integrated with large-scale experimental data. Mass spectrometry (MS)-based proteomics has identified and quantified tens of thousands of posttranslational modifications (PTMs), most of them of uncertain functional relevance. In this study, we determine the structural context of these PTMs and investigate how this information can be leveraged to pinpoint potential regulatory sites. Our analysis uncovers global patterns of PTM occurrence across folded and intrinsically disordered regions. We found that this information can help to distinguish regulatory PTMs from those marking improperly folded proteins. Interestingly, the human proteome contains thousands of proteins that have large folded domains linked by short, disordered regions that are strongly enriched in regulatory phosphosites. These include well-known kinase activation loops that induce protein conformational changes upon phosphorylation. This regulatory mechanism appears to be widespread in kinases but also occurs in other protein families such as solute carriers. It is not limited to phosphorylation but includes ubiquitination and acetylation sites as well. Furthermore, we performed three-dimensional proximity analysis, which revealed examples of spatial coregulation of different PTM types and potential PTM crosstalk. To enable the community to build upon these first analyses, we provide tools for 3D visualization of proteomics data and PTMs as well as python libraries for data accession and processing.

The Intricacy of the Viral-Human Protein Interaction Networks: Resources, Data, and Analyses

Viral infections are one of the major causes of human diseases that cause yearly millions of deaths and seriously threaten global health, as we have experienced with the COVID-19 pandemic. Numerous approaches have been adopted to understand viral diseases and develop pharmacological treatments. Among them, the study of virus-host protein-protein interactions is a powerful strategy to comprehend the molecular mechanisms employed by the virus to infect the host cells and to interact with their components. Experimental protein-protein interactions described in the scientific literature have been systematically captured into several molecular interaction databases. These data are organized in structured formats and can be easily downloaded by users to perform further bioinformatic and network studies. Network analysis of available virus-host interactomes allow us to understand how the host interactome is perturbed upon viral infection and what are the key host proteins targeted by the virus and the main cellular pathways that are subverted. In this review, we give an overview of publicly available viral-human protein-protein interactions resources and the community standards, curation rules and adopted ontologies. A description of the main virus-human interactome available is provided, together with the main network analyses that have been performed. We finally discuss the main limitations and future challenges to assess the quality and reliability of protein-protein interaction datasets and resources.

Exploring the conformational diversity of proteins

An artificial intelligence-based method can predict distinct conformational states of membrane transporters and receptors.

Substrate Recognition by the Peptidyl-(S)-2-mercaptoglycine Synthase TglHI during 3-Thiaglutamate Biosynthesis

3-Thiaglutamate is a recently identified amino acid analog originating from cysteine. During its biosynthesis, cysteinyl-tRNA is first enzymatically appended to the C-terminus of TglA, a 50-residue ribosomally translated peptide scaffold. After hydrolytic removal of the tRNA, this cysteine residue undergoes modification on the scaffold before eventual proteolysis of the nascent 3-thiaglutamyl residue to release 3-thiaglutamate and regenerate TglA. One of the modifications of TglACys requires a complex of two polypeptides, TglH and TglI, which uses nonheme iron and O₂ to catalyze the removal of the peptidyl-cysteine β-methylene group, oxidation of this Cβ atom to formate, and reattachment of the thiol group to the α carbon. Herein, we use in vitro transcription-coupled translation and expressed protein ligation to characterize the role of the TglA scaffold in TglHI recognition and determine the specificity of TglHI with respect to the C-terminal residues of its substrate TglACys. The results of these experiments establish a synthetically accessible TglACys fragment sufficient for modification by TglHI and identify the l-selenocysteine analog of TglACys, TglASec, as an inhibitor of TglHI. These insights as well as a predicted structure and native mass spectrometry data set the stage for deeper mechanistic investigation of the complex TglHI-catalyzed reaction.

Architecture of the human NALCN channelosome

NALCN regulates the resting membrane potential by mediating the Na⁺ leak current in neurons, and it functions as a channelosome in complex with FAM155A, UNC79, and UNC80. Dysfunction of the NALCN channelosome causes a broad range of neurological and developmental diseases called NALCN channelopathies in humans. How the auxiliary subunits, especially the two large components UNC79 and UNC80, assemble with NALCN and regulate its function remains unclear. Here we report an overall architecture of the human NALCN channelosome. UNC79 and UNC80 each adopt an S-shape super-helical structure consisting of HEAT and armadillo repeats, forming a super-coiled heterodimeric assembly in the cytoplasmic side, which may provide a scaffold for the binding of other potential modulators of the channelosome. The UNC79-UNC80 assembly specifically associates with the NALCN-FAM155A subcomplex through the intracellular II-III linker of NALCN. Disruptions of the interaction interfaces between UNC79 and UNC80, and between the II-III linker of NALCN and the UNC79-UNC80 assembly, significantly reduce the NALCN-mediated currents in HEK293T system, suggesting the importance of the UNC79-UNC80 assembly in regulating channelosome function. Cross-linking mass spectrometry analysis identified an additional calmodulin (CaM) bound in the carboxyl-terminal domain of NALCN. Our study thus provides a structural basis for understanding the unique assembly mechanism and functional regulation of the NALCN channelosome, and also provides an opportunity for the interpretation of many disease-related mutations in UNC80.

Naturally Occurring I81N Mutation in Human Cytochrome c Regulates Both Inherent Peroxidase Activity and Interactions with Neuroglobin

Human cytochrome c (hCyt c) is a crucial heme protein and plays an indispensable role in energy conversion and intrinsic apoptosis pathways. The sequence and structure of Cyt c were evolutionarily conserved and only a few naturally occurring mutants were detected in humans. Among those variable sites, position 81 was proposed to act as a peroxidase switch in the initiation stages of apoptosis. In this study, we show that Ile81 not only suppresses the intrinsic peroxidase activity but also is essential for Cyt c to interact with neuroglobin (Ngb), a potential protein partner. The kinetic assays showed that the peroxidase activity of the naturally occurring variant I81N was enhanced up to threefold under pH 5. The local stability of the Ω-loop D (residues 70-85) in the I81N variant was decreased. Moreover, the Alphafold2 program predicted that Ile81 forms stable contact with human Ngb. Meanwhile, the Ile81 to Asn81 missense mutation abolishes the interaction interface, resulting in a ∼40-fold decrease in binding affinity. These observations provide an insight into the structure-function relationship of the conserved Ile81 in vertebrate Cyt c.

Structural validation and assessment of AlphaFold2 predictions for centrosomal and centriolar proteins and their complexes

Obtaining the high-resolution structures of proteins and their complexes is a crucial aspect of understanding the mechanisms of life. Experimental structure determination methods are time-consuming, expensive and cannot keep pace with the growing number of protein sequences available through genomic DNA sequencing. Thus, the ability to accurately predict the structure of proteins from their sequence is a holy grail of structural and computational biology that would remove a bottleneck in our efforts to understand as well as rationally engineer living systems. Recent advances in protein structure prediction, in particular the breakthrough with the AI-based tool AlphaFold2 (AF2), hold promise for achieving this goal, but the practical utility of AF2 remains to be explored. Focusing on proteins with essential roles in centrosome and centriole biogenesis, we demonstrate the quality and usability of the AF2 prediction models and we show that they can provide important insights into the modular organization of two key players in this process, CEP192 and CEP44. Furthermore, we used the AF2 algorithm to elucidate and then experimentally validate previously unknown prime features in the structure of TTBK2 bound to CEP164, as well as the Chibby1-FAM92A complex for which no structural information was available to date. These findings have important implications in understanding the regulation and function of these complexes. Finally, we also discuss some practical limitations of AF2 and anticipate the implications for future research approaches in the centriole/centrosome field.

Structural Models for Roseolovirus U20 And U21: Non-Classical MHC-I Like Proteins From HHV-6A, HHV-6B, and HHV-7

Human roseolovirus U20 and U21 are type I membrane glycoproteins that have been implicated in immune evasion by interfering with recognition of classical and non-classical MHC proteins. U20 and U21 are predicted to be type I glycoproteins with extracytosolic immunoglobulin-like domains, but detailed structural information is lacking. AlphaFold and RoseTTAfold are next generation machine-learning-based prediction engines that recently have revolutionized the field of computational three-dimensional protein structure prediction. Here, we review the structural biology of viral immunoevasins and the current status of computational structure prediction algorithms. We use these computational tools to generate structural models for U20 and U21 proteins, which are predicted to adopt MHC-Ia-like folds with closed MHC platforms and immunoglobulin-like domains. We evaluate these structural models and place them within current understanding of the structural basis for viral immune evasion of T cell and natural killer cell recognition.

[Mechanisms of development of side effects and drug resistance to asparaginase and ways to overcome them]

Asparaginase is one of the most important chemotherapeutic agents against acute lymphoblastic leukemia, the most common form of blood cancer. To date, both asparaginases from E. coli and Dickeya dadantii (formerly known as Erwinia chrysanthemi), used in hematology, induce chemoresistance in cancer cells and side effects in the form of hypersensitivity of immune reactions. Leukemic cells may be resistant to asparaginase due to the increased activity of asparagine synthetase and other mechanisms associated with resistance to asparaginase. Therefore, the search for new sources of L-asparaginases with improved pharmacological properties remains a promising and prospective study. This article discusses the mechanisms of development of resistance and drug resistance to L-asparaginase, as well as possible ways to overcome them.

AF2Complex predicts direct physical interactions in multimeric proteins with deep learning

Accurate descriptions of protein-protein interactions are essential for understanding biological systems. Remarkably accurate atomic structures have been recently computed for individual proteins by AlphaFold2 (AF2). Here, we demonstrate that the same neural network models from AF2 developed for single protein sequences can be adapted to predict the structures of multimeric protein complexes without retraining. In contrast to common approaches, our method, AF2Complex, does not require paired multiple sequence alignments. It achieves higher accuracy than some complex protein-protein docking strategies and provides a significant improvement over AF-Multimer, a development of AlphaFold for multimeric proteins. Moreover, we introduce metrics for predicting direct protein-protein interactions between arbitrary protein pairs and validate AF2Complex on some challenging benchmark sets and the E. coli proteome. Lastly, using the cytochrome c biogenesis system I as an example, we present high-confidence models of three sought-after assemblies formed by eight members of this system.

Crystal Structures of Wolbachia CidA and CidB Reveal Determinants of Bacteria-induced Cytoplasmic Incompatibility and Rescue

Cytoplasmic incompatibility (CI) results when Wolbachia bacteria-infected male insects mate with uninfected females, leading to embryonic lethality. “Rescue” of viability occurs if the female harbors the same Wolbachia strain. CI is caused by linked pairs of Wolbachia genes called CI factors (CifA and CifB). The co-evolution of CifA-CifB pairs may account in part for the incompatibility patterns documented in insects infected with different Wolbachia strains, but the molecular mechanisms remain elusive. Here, we use X-ray crystallography and AlphaFold to analyze the CI factors from Wolbachia strain wMel called CidA^wMel and CidB^wMel. Substituting CidA^wMel interface residues with those from CidA^wPip (from strain wPip) enables the mutant protein to bind CidB^wPip and rescue CidB^wPip-induced yeast growth defects, supporting the importance of CifA-CifB interaction in CI rescue. Sequence divergence in CidA^wPip and CidB^wPip proteins affects their pairwise interactions, which may help explain the complex incompatibility patterns of mosquitoes infected with different wPip strains.

Wolbachia induced cytoplasmic incompatibility (CI) is caused by linked pairs of genes named cifA and cifB. Here, authors show that the residues at interfaces of the CidA-CidB complex is crucial for their binding and contribute to the diversity of CI.

An Updated Model of the Divisome: Regulation of the Septal Peptidoglycan Synthesis Machinery by the Divisome

The synthesis of a peptidoglycan septum is a fundamental part of bacterial fission and is driven by a multiprotein dynamic complex called the divisome. FtsW and FtsI are essential proteins that synthesize the peptidoglycan septum and are controlled by the regulatory FtsBLQ subcomplex and the activator FtsN. However, their mode of regulation has not yet been uncovered in detail. Understanding this process in detail may enable the development of new compounds to combat the rise in antibiotic resistance. In this review, recent data on the regulation of septal peptidoglycan synthesis is summarized and discussed. Based on structural models and the collected data, multiple putative interactions within FtsWI and with regulators are uncovered. This elaborates on and supports an earlier proposed model that describes active and inactive conformations of the septal peptidoglycan synthesis complex that are stabilized by these interactions. Furthermore, a new model on the spatial organization of the newly synthesized peptidoglycan and the synthesis complex is presented. Overall, the updated model proposes a balance between several allosteric interactions that determine the state of septal peptidoglycan synthesis.

Modeling the Dynamics of Protein-Protein Interfaces, How and Why?

Protein-protein assemblies act as a key component in numerous cellular processes. Their accurate modeling at the atomic level remains a challenge for structural biology. To address this challenge, several docking and a handful of deep learning methodologies focus on modeling protein-protein interfaces. Although the outcome of these methods has been assessed using static reference structures, more and more data point to the fact that the interaction stability and specificity is encoded in the dynamics of these interfaces. Therefore, this dynamics information must be taken into account when modeling and assessing protein interactions at the atomistic scale. Expanding on this, our review initially focuses on the recent computational strategies aiming at investigating protein-protein interfaces in a dynamic fashion using enhanced sampling, multi-scale modeling, and experimental data integration. Then, we discuss how interface dynamics report on the function of protein assemblies in globular complexes, in fuzzy complexes containing intrinsically disordered proteins, as well as in active complexes, where chemical reactions take place across the protein-protein interface.

Improved prediction of protein-protein interactions using AlphaFold2

Predicting the structure of interacting protein chains is a fundamental step towards understanding protein function. Unfortunately, no computational method can produce accurate structures of protein complexes. AlphaFold2, has shown unprecedented levels of accuracy in modelling single chain protein structures. Here, we apply AlphaFold2 for the prediction of heterodimeric protein complexes. We find that the AlphaFold2 protocol together with optimised multiple sequence alignments, generate models with acceptable quality (DockQ ≥ 0.23) for 63% of the dimers. From the predicted interfaces we create a simple function to predict the DockQ score which distinguishes acceptable from incorrect models as well as interacting from non-interacting proteins with state-of-art accuracy. We find that, using the predicted DockQ scores, we can identify 51% of all interacting pairs at 1% FPR.

Predicting the structure of protein complexes is extremely difficult. Here, authors apply AlphaFold2 with optimized multiple sequence alignments to model complexes of interacting proteins, enabling prediction of both if and how proteins interact with state-of-art accuracy.

Molecular Analysis of L-Asparaginases for Clarification of the Mechanism of Action and Optimization of Pharmacological Functions

L-asparaginases (EC 3.5.1.1) are a family of enzymes that catalyze the hydrolysis of L-asparagine to L-aspartic acid and ammonia. These proteins with different biochemical, physicochemical and pharmacological properties are found in many organisms, including bacteria, fungi, algae, plants and mammals. To date, asparaginases from E. coli and Dickeya dadantii (formerly known as Erwinia chrysanthemi) are widely used in hematology for the treatment of lymphoblastic leukemias. However, their medical use is limited by side effects associated with the ability of these enzymes to hydrolyze L-glutamine, as well as the development of immune reactions. To solve these issues, gene-editing methods to introduce amino-acid substitutions of the enzyme are implemented. In this review, we focused on molecular analysis of the mechanism of enzyme action and to optimize the antitumor activity.

Protein-Protein Interaction-Gaussian Accelerated Molecular Dynamics (PPI-GaMD): Characterization of Protein Binding Thermodynamics and Kinetics

Protein-protein interactions (PPIs) play key roles in many fundamental biological processes such as cellular signaling and immune responses. However, it has proven challenging to simulate repetitive protein association and dissociation in order to calculate binding free energies and kinetics of PPIs due to long biological timescales and complex protein dynamics. To address this challenge, we have developed a new computational approach to all-atom simulations of PPIs based on a robust Gaussian accelerated molecular dynamics (GaMD) technique. The method, termed "PPI-GaMD", selectively boosts interaction potential energy between protein partners to facilitate their slow dissociation. Meanwhile, another boost potential is applied to the remaining potential energy of the entire system to effectively model the protein's flexibility and rebinding. PPI-GaMD has been demonstrated on a model system of the ribonuclease barnase interactions with its inhibitor barstar. Six independent 2 μs PPI-GaMD simulations have captured repetitive barstar dissociation and rebinding events, which enable calculations of the protein binding thermodynamics and kinetics simultaneously. The calculated binding free energies and kinetic rate constants agree well with the experimental data. Furthermore, PPI-GaMD simulations have provided mechanistic insights into barstar binding to barnase, which involves long-range electrostatic interactions and multiple binding pathways, being consistent with previous experimental and computational findings of this model system. In summary, PPI-GaMD provides a highly efficient and easy-to-use approach for binding free energy and kinetics calculations of PPIs.

AbAdapt: an adaptive approach to predicting antibody-antigen complex structures from sequence

Motivation

The scoring of antibody-antigen docked poses starting from unbound homology models has not been systematically optimized for a large and diverse set of input sequences.

Results

To address this need, we have developed AbAdapt, a webserver that accepts antibody and antigen sequences, models their 3D structures, predicts epitope and paratope, and then docks the modeled structures using two established docking engines (Piper and Hex). Each of the key steps has been optimized by developing and training new machine-learning models. The sequences from a diverse set of 622 antibody-antigen pairs with known structure were used as inputs for leave-one-out cross-validation. The final set of cluster representatives included at least one 'Adequate' pose for 550/622 (88.4%) of the queries. The median (interquartile range) ranks of these 'Adequate' poses were 22 (5-77). Similar results were obtained on a holdout set of 100 unrelated antibody-antigen pairs. When epitopes were repredicted using docking-derived features for specific antibodies, the median ROC AUC increased from 0.679 to 0.720 in cross-validation and from 0.694 to 0.730 in the holdout set.

Availability and implementation

AbAdapt and related data are available at https://sysimm.org/abadapt/.

Supplementary information

Supplementary data are available at Bioinformatics Advances online.

Next-Generation Molecular Discovery: From Bottom-Up In Vivo and In Vitro Approaches to In Silico Top-Down Approaches for Therapeutics Neogenesis

Protein and drug engineering comprises a major part of the medical and research industries, and yet approaches to discovering and understanding therapeutic molecular interactions in biological systems rely on trial and error. The general approach to molecular discovery involves screening large libraries of compounds, proteins, or antibodies, or in vivo antibody generation, which could be considered "bottom-up" approaches to therapeutic discovery. In these bottom-up approaches, a minimal amount is known about the therapeutics at the start of the process, but through meticulous and exhaustive laboratory work, the molecule is characterised in detail. In contrast, the advent of "big data" and access to extensive online databases and machine learning technologies offers promising new avenues to understanding molecular interactions. Artificial intelligence (AI) now has the potential to predict protein structure at an unprecedented accuracy using only the genetic sequence. This predictive approach to characterising molecular structure-when accompanied by high-quality experimental data for model training-has the capacity to invert the process of molecular discovery and characterisation. The process has potential to be transformed into a top-down approach, where new molecules can be designed directly based on the structure of a target and the desired function, rather than performing screening of large libraries of molecular variants. This paper will provide a brief evaluation of bottom-up approaches to discovering and characterising biological molecules and will discuss recent advances towards developing top-down approaches and the prospects of this.

Challenges and Advantages of Accounting for Backbone Flexibility in Prediction of Protein-Protein Complexes

Predicting protein binding is a core problem of computational biophysics. That this objective can be partly achieved with some amount of success using docking algorithms based on rigid protein models is remarkable, although going further requires allowing for protein flexibility. However, accurately capturing the conformational changes upon binding remains an enduring challenge for docking algorithms. Here, we adapt our Upside folding model, where side chains are represented as multi-position beads, to explore how flexibility may impact predictions of protein-protein complexes. Specifically, the Upside model is used to investigate where backbone flexibility helps, which types of interactions are important, and what is the impact of coarse graining. These efforts also shed light on the relative challenges posed by folding and docking. After training the Upside energy function for docking, the model is competitive with the established all-atom methods. However, allowing for backbone flexibility during docking is generally detrimental, as the presence of comparatively minor (3-5 Å) deviations relative to the docked structure has a large negative effect on performance. While this issue appears to be inherent to current forcefield-guided flexible docking methods, systems involving the co-folding of flexible loops such as antibody-antigen complexes represent an interesting exception. In this case, binding is improved when backbone flexibility is allowed using the Upside model.

Cryo-EM advances in RNA structure determination

Cryo-electron microscopy (cryo-EM) has emerged as an unprecedented tool to resolve protein structures at atomic resolution. Structural insights of biological samples not accessible by conventional X-ray crystallography and NMR can be explored with cryo-EM because measurements are carried out under near-native crystal-free conditions, and large protein complexes with conformational and compositional heterogeneity are readily resolved. RNA has remained underexplored in cryo-EM, despite its essential role in various biological processes. This review highlights current challenges and recent progress in using cryo-EM single-particle analysis to determine protein-free RNA structures, enabled by improvement in sample preparation and integration of multiple structural and biochemical methods.

Prediction of Monomeric and Dimeric Structures of CYP102A1 Using AlphaFold2 and AlphaFold Multimer and Assessment of Point Mutation Effect on the Efficiency of Intra- and Interprotein Electron Transfer

The three-dimensional structure of monomers and homodimers of CYP102A1/WT (wild-type) proteins and their A83F and A83I mutant forms was predicted using the AlphaFold2 (AF2) and AlphaFold Multimer (AFMultimer) programs, which were compared with the rate constants of hydroxylation reactions of these enzyme forms to determine the efficiency of intra- and interprotein electron transport in the CYP102A1 hydroxylase system. The electron transfer rate constants (k_et), which determine the rate of indole hydroxylation by the CYP102A1 system, were calculated based on the distances (R) between donor-acceptor prosthetic groups (PG) FAD→FMN→HEME of these proteins using factor β, which describes an exponential decay from R the speed of electron transport (ET) according to the tunnelling mechanism. It was shown that the structure of monomers in the homodimer, calculated using the AlpfaFold Multimer program, is in good agreement with the experimental structures of globular domains (HEME-, FMN-, and FAD-domains) in CYP102A1/WT obtained by X-ray structural analysis, and the structure of isolated monomers predicted in AF2 does not coincide with the structure of monomers in the homodimer, although a high level of similarity in individual domains remains. The structures of monomers and homodimers of A83F and A83I mutants were also calculated, and their structures were compared with the wild-type protein. Significant differences in the structure of all isolated monomers with respect to the structures of monomers in homodimers were also found for them, and at the same time, insignificant differences were revealed for all homodimers. Comparative analysis for CYP102A1/WT between the calculated intra- and interprotein distances FAD→FMN→HEME and the rate constants of hydroxylation in these proteins showed that the distance between prosthetic groups both in the monomer and in the dimer allows the implementation of electron transfer between PGs, which is consistent with experimental literature data about k_cat. For the mutant form of monomer A83I, an increase in the distance between PGs was obtained, which can restrict electron transportation compared to WT; however, for the dimer of this protein, a decrease in the distance between PGs was observed compared to the WT form, which can lead to an increase in the electron transfer rate constant and, accordingly, k_cat. For the monomer and homodimer of the A83F mutant, the calculations showed an increase in the distance between the PGs compared to the WT form, which should have led to a decrease in the electron transfer rate, but at the same time, for the homodimer, the approach of the aromatic group F262 with heme can speed up transportation for this form and, accordingly, the rate of hydroxylation.

CACHE (Critical Assessment of Computational Hit-finding Experiments): A public–private partnership benchmarking initiative to enable the development of computational methods for hit-finding

One aspirational goal of computational chemistry is to predict potent and drug-like binders for any protein, such that only those that bind are synthesized. In this Roadmap, we describe the launch of Critical Assessment of Computational Hit-finding Experiments (CACHE), a public benchmarking project to compare and improve small molecule hit-finding algorithms through cycles of prediction and experimental testing. Participants will predict small molecule binders for new and biologically relevant protein targets representing different prediction scenarios. Predicted compounds will be tested rigorously in an experimental hub, and all predicted binders as well as all experimental screening data, including the chemical structures of experimentally tested compounds, will be made publicly available, and not subject to any intellectual property restrictions. The ability of a range of computational approaches to find novel binders will be evaluated, compared, and openly published. CACHE will launch 3 new benchmarking exercises every year. The outcomes will be better prediction methods, new small molecule binders for target proteins of importance for fundamental biology or drug discovery, and a major technological step towards achieving the goal of Target 2035, a global initiative to identify pharmacological probes for all human proteins.

Adenovirus Fibers as Ultra-Stable Vehicles for Intracellular Nanoparticle and Protein Delivery

Protein-based carriers are promising vehicles for the intracellular delivery of therapeutics. In this study, we designed and studied adenovirus protein fiber constructs with potential applications as carriers for the delivery of protein and nanoparticle cargoes. We used as a basic structural framework the fibrous shaft segment of the adenovirus fiber protein comprising of residues 61–392, connected to the fibritin foldon trimerization motif at the C-terminal end. A fourteen-amino-acid biotinylation sequence was inserted immediately after the N-terminal, His-tagged end of the construct in order to enable the attachment of a biotin moiety in vivo. We report herein that this His-tag biotinylated construct folds into thermally and protease-stable fibrous nanorods that can be internalized into cells and are not cytotoxic. Moreover, they can bind to proteins and nanoparticles through the biotin–streptavidin interaction and mediate their delivery to cells. We demonstrate that streptavidin-conjugated gold nanoparticles can be transported into NIH3T3 fibroblast and HeLa cancer cell lines. Furthermore, two streptavidin-conjugated model proteins, alkaline phosphatase and horseradish peroxidase can be delivered into the cell cytoplasm in their enzymatically active form. This work is aimed at establishing the proof-of-principle for the rational engineering of diverse functionalities onto the initial protein structural framework and the use of adenovirus fiber-based proteins as nanorods for the delivery of nanoparticles and model proteins. These constructs could constitute a stepping stone for the development of multifunctional and modular fibrous nanorod platforms that can be tailored to applications at the sequence level.

Application of Homology Modeling by Enhanced Profile-Profile Alignment and Flexible-Fitting Simulation to Cryo-EM Based Structure Determination

Application of cryo-electron microscopy (cryo-EM) is crucially important for ascertaining the atomic structure of large biomolecules such as ribosomes and protein complexes in membranes. Advances in cryo-EM technology and software have made it possible to obtain data with near-atomic resolution, but the method is still often capable of producing only a density map with up to medium resolution, either partially or entirely. Therefore, bridging the gap separating the density map and the atomic model is necessary. Herein, we propose a methodology for constructing atomic structure models based on cryo-EM maps with low-to-medium resolution. The method is a combination of sensitive and accurate homology modeling using our profile-profile alignment method with a flexible-fitting method using molecular dynamics simulation. As described herein, this study used benchmark applications to evaluate the model constructions of human two-pore channel 2 (one target protein in CASP13 with its structure determined using cryo-EM data) and the overall structure of Enterococcus hirae V-ATPase complex.

Latent TGFβ complexes are transglutaminase cross-linked to fibrillin to facilitate TGFβ activation

•

TGFβ is regulated via the formation latent complexes in the extracellular matrix.

•

Fibrillin-1 is a substrate for transglutaminase-2 which forms a covalent link between fibrillin-1 and latent TGFβ complexes.

•

Formation of the cross-link increases TGFβ activation in cell-based assays.

•

Fibrillin may direct the latent TGFβ complexes to the cell surface for activation.

•

The structure of the cross-linked LTBP1-fibrillin complex has a perpendicular arrangement to enable bridging long-range interactions between the matrix and cell surface.

TGFβ superfamily members are potent growth factors in the extracellular matrix with essential roles in all aspects of cellular behaviour. Latent TGFβ binding proteins (LTBPs) are co-expressed with TGFβ, essential for correct folding and secretion of the growth factor, to form large latent complexes. These large latent complexes bind extracellular proteins such as fibrillin for sequestration of TGFβ in the matrix, essential for normal tissue function, and dysregulated TGFβ signalling is a hallmark of many fibrillinopathies. Transglutaminase-2 (TG2) cross-linking of LTBPs is known to play a role in TGFβ activation but the underlying molecular mechanisms are not resolved. Here we show that fibrillin is a matrix substrate for TG2 and that TG2 cross-linked complexes can be formed between fibrillin and LTBP-1 and -3, and their latent TGFβ complexes. The structure of the fibrillin-LTBP1 complex shows that the two elongated proteins interact in a perpendicular arrangement which would allow them to form distal interactions between the matrix and the cell surface. Formation of the cross-link with fibrillin does not change the interaction between latent TGFβ and integrin αVβ6 but does increase TGFβ activation in cell-based assays. The activating effect may be due to direction of the latent complexes to the cell surface by fibrillin, as competition with heparan sulphate can ameliorate the activating effect. Together, these data support that TGFβ activation can be enhanced by covalent tethering of LTBPs to the matrix via fibrillin.