Improved prediction of protein-protein interactions using AlphaFold2

AlphaCRV: a pipeline for identifying accurate binder topologies in mass-modeling with AlphaFold

Motivation

The speed and accuracy of deep learning-based structure prediction algorithms make it now possible to perform in silico "pull-downs" to identify protein-protein interactions on a proteome-wide scale. However, on such a large scale, existing scoring algorithms are often insufficient to discriminate biologically relevant interactions from false positives.

Results

Here, we introduce AlphaCRV, a Python package that helps identify correct interactors in a one-against-many AlphaFold screen by clustering, ranking, and visualizing conserved binding topologies, based on protein sequence and fold.

Availability and implementation

AlphaCRV is a Python package for Linux, freely available at https://github.com/strubelab/AlphaCRV.

USP37 prevents premature disassembly of stressed replisomes by TRAIP

The E3 ubiquitin ligase TRAIP associates with the replisome and helps this molecular machine deal with replication stress. Thus, TRAIP promotes DNA inter-strand crosslink repair by triggering the disassembly of CDC45-MCM2-7-GINS (CMG) helicases that have converged on these lesions. However, disassembly of single CMGs that have stalled temporarily would be deleterious, suggesting that TRAIP must be carefully regulated. Here, we demonstrate that human cells lacking the de-ubiquitylating enzyme USP37 are hypersensitive to topoisomerase poisons and other replication stress-inducing agents. We further show that TRAIP loss rescues the hypersensitivity of USP37 knockout cells to topoisomerase inhibitors. In Xenopus egg extracts depleted of USP37, TRAIP promotes premature CMG ubiquitylation and disassembly when converging replisomes stall. Finally, guided by AlphaFold-Multimer, we discovered that binding to CDC45 mediates USP37's response to topological stress. In conclusion, we propose that USP37 protects genome stability by preventing TRAIP-dependent CMG unloading when replication stress impedes timely termination.

Bound ion effects: Using machine learning method to study the kinesin Ncd's binding with microtubule

Drosophila Ncd proteins are motor proteins that play important roles in spindle organization. Ncd and the tubulin dimer are highly charged. Thus, it is crucial to investigate Ncd-tubulin dimer interactions in the presence of ions, especially ions that are bound or restricted at the Ncd-tubulin dimer binding interfaces. To consider the ion effects, widely used implicit solvent models treat ions implicitly in the continuous solvent environment without focusing on the individual ions' effects. But highly charged biomolecules such as the Ncd and tubulin dimer may capture some ions at highly charged regions as bound ions. Such bound ions are restricted to their binding sites; thus, they can be treated as part of the biomolecules. By applying multiscale computational methods, including the machine-learning-based Hybridizing Ions Treatment-2 program, molecular dynamics simulations, DelPhi, and DelPhiForce, we studied the interaction between the Ncd motor domain and the tubulin dimer using a hybrid solvent model, which considers the bound ions explicitly and the other ions implicitly in the solvent environment. To identify the importance of treating bound ions explicitly, we also performed calculations using the implicit solvent model without considering the individual bound ions. We found that the calculations of the electrostatic features differ significantly between those of the hybrid solvent model and the pure implicit solvent model. The analyses show that treating bound ions at highly charged regions explicitly is crucial for electrostatic calculations. This work proposes a machine-learning-based approach to handle the bound ions using the hybrid solvent model. Such an approach is not only capable of handling kinesin-tubulin complexes but is also appropriate for other highly charged biomolecules, such as DNA/RNA, viral capsid proteins, etc.

SEPALLATA-driven MADS transcription factor tetramerization is required for inner whorl floral organ development

MADS transcription factors are master regulators of plant reproduction and flower development. The SEPALLATA (SEP) subfamily of MADS transcription factors is required for the development of floral organs and plays roles in inflorescence architecture and development of the floral meristem. SEPALLATAs act as organizers of MADS complexes, forming both heterodimers and heterotetramers in vitro. To date, the MADS complexes characterized in angiosperm floral organ development contain at least 1 SEPALLATA protein. Whether DNA binding by SEPALLATA-containing dimeric MADS complexes is sufficient for launching floral organ identity programs, however, is not clear as only defects in floral meristem determinacy were observed in tetramerization-impaired SEPALLATA mutant proteins. Here, we used a combination of genome-wide-binding studies, high-resolution structural studies of the SEP3/AGAMOUS (AG) tetramerization domain, structure-based mutagenesis and complementation experiments in Arabidopsis (Arabidopsis thaliana) sep1 sep2 sep3 and sep1 sep2 sep3 ag-4 plants transformed with versions of SEP3 encoding tetramerization mutants. We demonstrate that while SEP3 heterodimers can bind DNA both in vitro and in vivo and recognize the majority of SEP3 wild-type-binding sites genome-wide, tetramerization is required not only for floral meristem determinacy but also for floral organ identity in the second, third, and fourth whorls.

Polypharmacology prediction: the long road toward comprehensively anticipating small-molecule selectivity to de-risk drug discovery

INTRODUCTION

Small molecules often bind to multiple targets, a behavior termed polypharmacology. Anticipating polypharmacology is essential for drug discovery since unknown off-targets can modulate safety and efficacy - profoundly affecting drug discovery success. Unfortunately, experimental methods to assess selectivity present significant limitations and drugs still fail in the clinic due to unanticipated off-targets. Computational methods are a cost-effective, complementary approach to predict polypharmacology.

AREAS COVERED

This review aims to provide a comprehensive overview of the state of polypharmacology prediction and discuss its strengths and limitations, covering both classical cheminformatics methods and bioinformatic approaches. The authors review available data sources, paying close attention to their different coverage. The authors then discuss major algorithms grouped by the types of data that they exploit using selected examples.

EXPERT OPINION

Polypharmacology prediction has made impressive progress over the last decades and contributed to identify many off-targets. However, data incompleteness currently limits most approaches to comprehensively predict selectivity. Moreover, our limited agreement on model assessment challenges the identification of the best algorithms - which at present show modest performance in prospective real-world applications. Despite these limitations, the exponential increase of multidisciplinary Big Data and AI hold much potential to better polypharmacology prediction and de-risk drug discovery.

Nonstructural protein 4 of human norovirus self-assembles into various membrane-bridging multimers

Single-stranded, positive-sense RNA ((+)RNA) viruses replicate their genomes in virus-induced intracellular membrane compartments. (+)RNA viruses dedicate a significant part of their small genomes (a few thousands to a few tens of thousands of bases) to the generation of these compartments by encoding membrane-interacting proteins and/or protein domains. Noroviruses are a very diverse genus of (+)RNA viruses including human and animal pathogens. Human noroviruses are the major cause of acute gastroenteritis worldwide, with genogroup II genotype 4 (GII.4) noroviruses accounting for the vast majority of infections. Three viral proteins encoded in the N terminus of the viral replication polyprotein direct intracellular membrane rearrangements associated with norovirus replication. Of these three, nonstructural protein 4 (NS4) seems to be the most important, although its exact functions in replication organelle formation are unknown. Here, we produce, purify, and characterize GII.4 NS4. AlphaFold modeling combined with experimental data refines and corrects our previous crude structural model of NS4. Using simple artificial liposomes, we report an extensive characterization of the membrane properties of NS4. We find that NS4 self-assembles and thereby bridges liposomes together. Cryo-EM, NMR, and membrane flotation show formation of several distinct NS4 assemblies, at least two of them bridging pairs of membranes together in different fashions. Noroviruses belong to (+)RNA viruses whose replication compartment is extruded from the target endomembrane and generates double-membrane vesicles. Our data establish that the 21-kDa GII.4 human norovirus NS4 can, in the absence of any other factor, recapitulate in tubo several features, including membrane apposition, that occur in such processes.

Monoclonal antibody development for early detection of ASFV I73R protein: Identification of a linear antigenic epitope

African swine fever virus (ASFV), which was first identified in northern China in 2018, causes high mortality in pigs. Since the I73R protein in ASFV is abundantly expressed during the early phase of virus replication, it can be used as a target protein for early diagnosis. In this study, the I73R protein of ASFV was expressed, and we successfully prepared a novel monoclonal antibody (mAb), 8G11D7, that recognizes this protein. Through both indirect immunofluorescence and Western blotting assays, we demonstrated that 8G11D7 can detect ASFV strains. By evaluating the binding of the antibody to a series of I73R-truncated peptides, the definitive epitope recognized by the monoclonal antibody 8G11D7 was determined to be 58 DKTNTIYPP 66. Bioinformatic analysis revealed that the antigenic epitope had a high antigenic index and conservatism. This study contributes to a deeper understanding of ASFV protein structure and function, helping establish ASFV-specific detection method.

Discovery of potential antidiabetic peptides using deep learning

Antidiabetic peptides (ADPs), peptides with potential antidiabetic activity, hold significant importance in the treatment and control of diabetes. Despite their therapeutic potential, the discovery and prediction of ADPs remain challenging due to limited data, the complex nature of peptide functions, and the expensive and time-consuming nature of traditional wet lab experiments. This study aims to address these challenges by exploring methods for the discovery and prediction of ADPs using advanced deep learning techniques. Specifically, we developed two models: a single-channel CNN and a three-channel neural network (CNN + RNN + Bi-LSTM). ADPs were primarily gathered from the BioDADPep database, alongside thousands of non-ADPs sourced from anticancer, antibacterial, and antiviral peptide datasets. Subsequently, data preprocessing was performed with the evolutionary scale model (ESM-2), followed by model training and evaluation through 10-fold cross-validation. Furthermore, this work collected a series of newly published ADPs as an independent test set through literature review, and found that the CNN model achieved the highest accuracy (90.48 %) in predicting the independent test set, surpassing existing ADP prediction tools. Finally, the application of the model was considered. SeqGAN was used to generate new candidate ADPs, followed by screening with the constructed CNN model. Selected peptides were then evaluated using physicochemical property prediction and structural forecasts for pharmaceutical potential. In summary, this study not only established robust ADP prediction models but also employed these models to screen a batch of potential ADPs, addressing a critical need in the field of peptide-based antidiabetic research.

Plastid HSP90C C-terminal extension region plays a regulatory role in chaperone activity and client binding

HSP90Cs are essential molecular chaperones localized in the plastid stroma that maintain protein homeostasis and assist the import and thylakoid transport of chloroplast proteins. While HSP90C contains all conserved domains as an HSP90 family protein, it also possesses a unique feature in its variable C-terminal extension (CTE) region. This study elucidated the specific function of this HSP90C CTE region. Our phylogenetic analyses revealed that this intrinsically disordered region contains a highly conserved DPW motif in the green lineages. With biochemical assays, we showed that the CTE is required for the chaperone to effectively interact with client proteins PsbO1 and LHCB2 to regulate ATP-independent chaperone activity and to effectuate its ATP hydrolysis. The CTE truncation mutants could support plant growth and development reminiscing the wild type under normal conditions except for a minor phenotype in cotyledon when expressed at a level comparable to wild type. However, higher HSP90C expression was observed to correlate with a stronger response to specific photosystem II inhibitor DCMU, and CTE truncations dampened the response. Additionally, when treated with lincomycin to inhibit chloroplast protein translation, CTE truncation mutants showed a delayed response to PsbO1 expression repression, suggesting its role in chloroplast retrograde signaling. Our study therefore provides insights into the mechanism of HSP90C in client protein binding and the regulation of green chloroplast maturation and function, especially under stress conditions.

Structures prediction and replica exchange molecular dynamics simulations of α-synuclein: A case study for intrinsically disordered proteins

In recent years, a variety of three-dimensional structure prediction tools, including AlphaFold2, AlphaFold3, I-TASSER, C-I-TASSER, Phyre2, ESMFold, and RoseTTAFold, have been employed in the investigation of intrinsically disordered proteins. However, a comprehensive validation of these tools specifically for intrinsically disordered proteins has yet to be conducted. In this study, we utilize AlphaFold2, AlphaFold3, I-TASSER, C-I-TASSER, Phyre2, ESMFold, and RoseTTAFold to predict the structure of a model intrinsically disordered α-synuclein protein. Additionally, extensive replica exchange molecular dynamics simulations of the intrinsically disordered protein are conducted. The resulting structures from both structure prediction tools and replica exchange molecular dynamics simulations are analyzed for radius of gyration, secondary and tertiary structure properties, as well as Cα and Hα chemical shift values. A comparison of the obtained results with experimental data reveals that replica exchange molecular dynamics simulations provide results in excellent agreement with experimental observations. However, none of the structure prediction tools utilized in this study can fully capture the structural characteristics of the model intrinsically disordered protein. This study shows that a cluster of ensembles are required for intrinsically disordered proteins. Artificial-intelligence based structure prediction tools such as AlphaFold3 and C-I-TASSER could benefit from stochastic sampling or Monte Carlo simulations for generating an ensemble of structures for intrinsically disordered proteins.

Unveiling the evolution of policies for enhancing protein structure predictions: A comprehensive analysis

Predicting protein structure is both fascinating and formidable, playing a crucial role in structure-based drug discovery and unraveling diseases with elusive origins. The Critical Assessment of Protein Structure Prediction (CASP) serves as a biannual battleground where global scientists converge to untangle the intricate relationships within amino acid chains. Two primary methods, Template-Based Modeling (TBM) and Template-Free (TF) strategies, dominate protein structure prediction. The trend has shifted towards Template-Free predictions due to their broader sequence coverage with fewer templates. The predictive process can be broadly classified into contact map, binned-distance, and real-valued distance predictions, each with distinctive strengths and limitations manifested through tailored loss functions. We have also introduced revolutionary end-to-end, and all-atom diffusion-based techniques that have transformed protein structure predictions. Recent advancements in deep learning techniques have significantly improved prediction accuracy, although the effectiveness is contingent upon the quality of input features derived from natural bio-physiochemical attributes and Multiple Sequence Alignments (MSA). Hence, the generation of high-quality MSA data holds paramount importance in harnessing informative input features for enhanced prediction outcomes. Remarkable successes have been achieved in protein structure prediction accuracy, however not enough for what structural knowledge was intended to, which implies need for development in some other aspects of the predictions. In this regard, scientists have opened other frontiers for protein structural prediction. The utilization of subsampling in multiple sequence alignment (MSA) and protein language modeling appears to be particularly promising in enhancing the accuracy and efficiency of predictions, ultimately aiding in drug discovery efforts. The exploration of predicting protein complex structure also opens up exciting opportunities to deepen our knowledge of molecular interactions and design therapeutics that are more effective. In this article, we have discussed the vicissitudes that the scientists have gone through to improve prediction accuracy, and examined the effective policies in predicting from different aspects, including the construction of high quality MSA, providing informative input features, and progresses in deep learning approaches. We have also briefly touched upon transitioning from predicting single-chain protein structures to predicting protein complex structures. Our findings point towards promoting open research environments to support the objectives of protein structure prediction.

Studying protein-protein interactions: Latest and most popular approaches

PPIs, or protein-protein interactions, are essential for many biological processes. According to the findings, abnormal PPIs have been linked to several diseases, such as cancer and infectious and neurological disorders. Consequently, focusing on PPIs is a path toward disease treatment and a crucial tool for producing novel medications. Many methods exist to investigate PPIs, including low- and high-throughput studies. Since many PPIs have been discovered using in vitro and in vivo experimental approaches, the use of computational methods to predict PPIs has grown due to the expanding scale of PPI data and the intrinsic complexity of interacting mechanisms. Recognizing PPI networks offers a systematic means of predicting protein functions, and pathways that are included. These investigations can help uncover the underlying molecular mechanisms of complex phenotypes and clarify the biological processes related to health and diseases. Therefore, our goal in this study is to provide an overview of the latest and most popular approaches for investigating PPIs. We also overview some important clinical approaches based on the PPIs and how these interactions can be targeted.

Predicting protein conformational motions using energetic frustration analysis and AlphaFold2

Proteins perform their biological functions through motion. Although high throughput prediction of the three-dimensional static structures of proteins has proved feasible using deep-learning-based methods, predicting the conformational motions remains a challenge. Purely data-driven machine learning methods encounter difficulty for addressing such motions because available laboratory data on conformational motions are still limited. In this work, we develop a method for generating protein allosteric motions by integrating physical energy landscape information into deep-learning-based methods. We show that local energetic frustration, which represents a quantification of the local features of the energy landscape governing protein allosteric dynamics, can be utilized to empower AlphaFold2 (AF2) to predict protein conformational motions. Starting from ground state static structures, this integrative method generates alternative structures as well as pathways of protein conformational motions, using a progressive enhancement of the energetic frustration features in the input multiple sequence alignment sequences. For a model protein adenylate kinase, we show that the generated conformational motions are consistent with available experimental and molecular dynamics simulation data. Applying the method to another two proteins KaiB and ribose-binding protein, which involve large-amplitude conformational changes, can also successfully generate the alternative conformations. We also show how to extract overall features of the AF2 energy landscape topography, which has been considered by many to be black box. Incorporating physical knowledge into deep-learning-based structure prediction algorithms provides a useful strategy to address the challenges of dynamic structure prediction of allosteric proteins.

AlphaFold predictions of fold-switched conformations are driven by structure memorization

Recent work suggests that AlphaFold (AF)-a deep learning-based model that can accurately infer protein structure from sequence-may discern important features of folded protein energy landscapes, defined by the diversity and frequency of different conformations in the folded state. Here, we test the limits of its predictive power on fold-switching proteins, which assume two structures with regions of distinct secondary and/or tertiary structure. We find that (1) AF is a weak predictor of fold switching and (2) some of its successes result from memorization of training-set structures rather than learned protein energetics. Combining >280,000 models from several implementations of AF2 and AF3, a 35% success rate was achieved for fold switchers likely in AF's training sets. AF2's confidence metrics selected against models consistent with experimentally determined fold-switching structures and failed to discriminate between low and high energy conformations. Further, AF captured only one out of seven experimentally confirmed fold switchers outside of its training sets despite extensive sampling of an additional ~280,000 models. Several observations indicate that AF2 has memorized structural information during training, and AF3 misassigns coevolutionary restraints. These limitations constrain the scope of successful predictions, highlighting the need for physically based methods that readily predict multiple protein conformations.

CDCA7 is an evolutionarily conserved hemimethylated DNA sensor in eukaryotes

Mutations of the SNF2 family ATPase HELLS and its activator CDCA7 cause immunodeficiency, centromeric instability, and facial anomalies syndrome, characterized by DNA hypomethylation at heterochromatin. It remains unclear why CDCA7-HELLS is the sole nucleosome remodeling complex whose deficiency abrogates the maintenance of DNA methylation. We here identify the unique zinc-finger domain of CDCA7 as an evolutionarily conserved hemimethylation-sensing zinc finger (HMZF) domain. Cryo-electron microscopy structural analysis of the CDCA7-nucleosome complex reveals that the HMZF domain can recognize hemimethylated CpG in the outward-facing DNA major groove within the nucleosome core particle, whereas UHRF1, the critical activator of the maintenance methyltransferase DNMT1, cannot. CDCA7 recruits HELLS to hemimethylated chromatin and facilitates UHRF1-mediated H3 ubiquitylation associated with replication-uncoupled maintenance DNA methylation. We propose that the CDCA7-HELLS nucleosome remodeling complex assists the maintenance of DNA methylation on chromatin by sensing hemimethylated CpG that is otherwise inaccessible to UHRF1 and DNMT1.

Development and tissue specific expression of RAPGEF1 (C3G) transcripts having exons encoding disordered segments with predicted regulatory function

BACKGROUND

The ubiquitously expressed Guanine nucleotide exchange factor, RAPGEF1 (C3G), is essential for early development of mouse embryos. It functions to regulate gene expression and cytoskeletal reorganization, thereby controlling cell proliferation and differentiation. While multiple transcripts have been predicted, their expression in mouse tissues has not been investigated in detail.

METHODS & RESULTS

Full length RAPGEF1 isoforms primarily arise due to splicing at two hotspots, one involving exon-3, and the other involving exons 12-14 incorporating amino acids immediately following the Crk binding region of the protein. These isoforms vary in expression across embryonic and adult organs. We detected the presence of unannotated, and unpredicted transcripts with incorporation of cassette exons in various combinations, specifically in the heart, brain, testis and skeletal muscle. Isoform switching was detected as myocytes in culture and mouse embryonic stem cells were differentiated to form myotubes, and embryoid bodies respectively. The cassette exons encode a serine-rich polypeptide chain, which is intrinsically disordered, and undergoes phosphorylation. In silico structural analysis using AlphaFold indicated that the presence of cassette exons alters intra-molecular interactions, important for regulating catalytic activity. LZerD based docking studies predicted that the isoforms with one or more cassette exons differ in interaction with their target GTPase, RAP1A.

CONCLUSIONS

Our results demonstrate the expression of novel RAPGEF1 isoforms, and predict cassette exon inclusion as an additional means of regulating RAPGEF1 activity in various tissues and during differentiation.

MS2/GmAMS1 encodes a bHLH transcription factor important for tapetum degeneration in soybean

KEY MESSAGE

GmAMS1 is the only functional AMS and works with GmTDF1-1 and GmMS3 to orchestrate the tapetum degeneration in soybean. Heterosis could significantly increase the production of major crops as well as soybean [Glycine max (L.) Merr.]. Stable male-sterile/female-fertile mutants including ms2 are useful resources to apply in soybean hybrid production. Here, we identified the detailed mutated sites of two classic mutants ms2 (Eldorado) and ms2 (Ames) in MS2/GmAMS1 via the high-throughput sequencing method. Subsequently, we verified that GmAMS1, a bHLH transcription factor, is the only functional AMS member in soybean through the complementary experiment in Arabidopsis; and elucidated the dysfunction of its homolog GmAMS2 is caused by the premature stop codon in the gene's coding sequence. Further qRT-PCR analysis and protein-protein interaction assays indicated GmAMS1 is required for expressing downstream members in the putative DYT1-TDF1-AMS-MYB80/MYB103/MS188-MS1 cascade module, and might regulate the upstream members in a feedback mechanism. GmAMS1 could interact with GmTDF1-1 and GmMS3 via different region, which contributes to dissect the mechanism in the tapetum degeneration process. Additionally, as a core member in the conserved cascade module controlling the tapetum development and degeneration, AMS is conservatively present in all land plant lineages, implying that AMS-mediated signaling pathway has been established before land plants diverged, which provides further insight into the tapetal evolution.

Translation efficiency covariation across cell types is a conserved organizing principle of mammalian transcriptomes

Characterization of shared patterns of RNA expression between genes across conditions has led to the discovery of regulatory networks and novel biological functions. However, it is unclear if such coordination extends to translation, a critical step in gene expression. Here, we uniformly analyzed 3,819 ribosome profiling datasets from 117 human and 94 mouse tissues and cell lines. We introduce the concept of Translation Efficiency Covariation (TEC), identifying coordinated translation patterns across cell types. We nominate potential mechanisms driving shared patterns of translation regulation. TEC is conserved across human and mouse cells and helps uncover gene functions. Moreover, our observations indicate that proteins that physically interact are highly enriched for positive covariation at both translational and transcriptional levels. Our findings establish translational covariation as a conserved organizing principle of mammalian transcriptomes.

Computational structural prediction and chemical inhibition of the human mitochondrial pyruvate carrier protein heterodimer complex

The mitochondrial pyruvate carrier (MPC) plays a role in numerous diseases including neurodegeneration, metabolically dependent cancers, and the development of insulin resistance. Several previous studies in genetic mouse models or with existing inhibitors suggest that inhibition of the MPC could be used as a viable therapeutic strategy in these diseases. However, the MPC's structure is unknown, making it difficult to screen for and develop therapeutically viable inhibitors. Currently known MPC inhibitors would make for poor drugs due to their poor pharmacokinetic properties, or in the case of the thiazolidinediones (TZDs), off-target specificity for peroxisome-proliferator activated receptor gamma (PPARγ) leads to unwanted side effects. In this study, we develop several structural models for the MPC heterodimer complex and investigate the chemical interactions required for the binding of these known inhibitors to MPC and PPARγ. Based on these models, the MPC most likely takes on outward-facing (OF) and inward-facing (IF) conformations during pyruvate transport, and inhibitors likely plug the carrier to inhibit pyruvate transport. Although some chemical interactions are similar between MPC and PPARγ binding, there is likely enough difference to reduce PPARγ specificity for future development of novel, more specific MPC inhibitors.

ARID3C Acts as a Regulator of Monocyte-to-Macrophage Differentiation Interacting with NPM1

ARID3C is a protein located on human chromosome 9 and expressed at low levels in various organs, yet its biological function has not been elucidated. In this study, we investigated both the cellular localization and function of ARID3C. Employing a combination of LC-MS/MS and deep learning techniques, we identified NPM1 as a binding partner for ARID3C's nuclear shuttling. ARID3C was found to predominantly localize with the nucleus, where it functioned as a transcription factor for genes STAT3, STAT1, and JUNB, thereby facilitating monocyte-to-macrophage differentiation. The precise binding sites between ARID3C and NPM1 were predicted by AlphaFold2. Mutating this binding site prevented ARID3C from interacting with NPM1, resulting in its retention in the cytoplasm instead of translocation to the nucleus. Consequently, ARID3C lost its ability to bind to the promoters of target genes, leading to a loss of monocyte-to-macrophage differentiation. Collectively, our findings indicate that ARID3C forms a complex with NPM1 to translocate to the nucleus, acting as a transcription factor that promotes the expression of the genes involved in monocyte-to-macrophage differentiation.

MAGPIE: An interactive tool for visualizing and analyzing protein-ligand interactions

Quantitative tools to compile and analyze biomolecular interactions among chemically diverse binding partners would improve therapeutic design and aid in studying molecular evolution. Here we present Mapping Areas of Genetic Parsimony In Epitopes (MAGPIE), a publicly available software package for simultaneously visualizing and analyzing thousands of interactions between a single protein or small molecule ligand (the "target") and all of its protein binding partners ("binders"). MAGPIE generates an interactive three-dimensional visualization from a set of protein complex structures that share the target ligand, as well as sequence logo-style amino acid frequency graphs that show all the amino acids from the set of protein binders that interact with user-defined target ligand positions or chemical groups. MAGPIE highlights all the salt bridge and hydrogen bond interactions made by the target in the visualization and as separate amino acid frequency graphs. Finally, MAGPIE collates the most common target-binder interactions as a list of "hotspots," which can be used to analyze trends or guide the de novo design of protein binders. As an example of the utility of the program, we used MAGPIE to probe how different antibody fragments bind a viral antigen; how a common metabolite binds diverse protein partners; and how two ligands bind orthologs of a well-conserved glycolytic enzyme for a detailed understanding of evolutionarily conserved interactions involved in its activation and inhibition. MAGPIE is implemented in Python 3 and freely available at https://github.com/glasgowlab/MAGPIE, along with sample datasets, usage examples, and helper scripts to prepare input structures.

Integrating Large-Scale Protein Structure Prediction into Human Genetics Research

The last five years have seen impressive progress in deep learning models applied to protein research. Most notably, sequence-based structure predictions have seen transformative gains in the form of AlphaFold2 and related approaches. Millions of missense protein variants in the human population lack annotations, and these computational methods are a valuable means to prioritize variants for further analysis. Here, we review the recent progress in deep learning models applied to the prediction of protein structure and protein variants, with particular emphasis on their implications for human genetics and health. Improved prediction of protein structures facilitates annotations of the impact of variants on protein stability, protein-protein interaction interfaces, and small-molecule binding pockets. Moreover, it contributes to the study of host-pathogen interactions and the characterization of protein function. As genome sequencing in large cohorts becomes increasingly prevalent, we believe that better integration of state-of-the-art protein informatics technologies into human genetics research is of paramount importance.

PPTC7 antagonizes mitophagy by promoting BNIP3 and NIX degradation via SCFFBXL4

Mitophagy must be carefully regulated to ensure that cells maintain appropriate numbers of functional mitochondria. The SCFFBXL4 ubiquitin ligase complex suppresses mitophagy by controlling the degradation of BNIP3 and NIX mitophagy receptors, and FBXL4 mutations result in mitochondrial disease as a consequence of elevated mitophagy. Here, we reveal that the mitochondrial phosphatase PPTC7 is an essential cofactor for SCFFBXL4-mediated destruction of BNIP3 and NIX, suppressing both steady-state and induced mitophagy. Disruption of the phosphatase activity of PPTC7 does not influence BNIP3 and NIX turnover. Rather, a pool of PPTC7 on the mitochondrial outer membrane acts as an adaptor linking BNIP3 and NIX to FBXL4, facilitating the turnover of these mitophagy receptors. PPTC7 accumulates on the outer mitochondrial membrane in response to mitophagy induction or the absence of FBXL4, suggesting a homoeostatic feedback mechanism that attenuates high levels of mitophagy. We mapped critical residues required for PPTC7-BNIP3/NIX and PPTC7-FBXL4 interactions and their disruption interferes with both BNIP3/NIX degradation and mitophagy suppression. Collectively, these findings delineate a complex regulatory mechanism that restricts BNIP3/NIX-induced mitophagy.

OpenFold: retraining AlphaFold2 yields new insights into its learning mechanisms and capacity for generalization

AlphaFold2 revolutionized structural biology with the ability to predict protein structures with exceptionally high accuracy. Its implementation, however, lacks the code and data required to train new models. These are necessary to (1) tackle new tasks, like protein-ligand complex structure prediction, (2) investigate the process by which the model learns and (3) assess the model's capacity to generalize to unseen regions of fold space. Here we report OpenFold, a fast, memory efficient and trainable implementation of AlphaFold2. We train OpenFold from scratch, matching the accuracy of AlphaFold2. Having established parity, we find that OpenFold is remarkably robust at generalizing even when the size and diversity of its training set is deliberately limited, including near-complete elisions of classes of secondary structure elements. By analyzing intermediate structures produced during training, we also gain insights into the hierarchical manner in which OpenFold learns to fold. In sum, our studies demonstrate the power and utility of OpenFold, which we believe will prove to be a crucial resource for the protein modeling community.

Chemical cross-linking and mass spectrometry enabled systems-level structural biology

Structural information on protein-protein interactions (PPIs) is essential for improved understanding of regulatory interactome networks that confer various physiological and pathological responses. Additionally, maladaptive PPIs constitute desirable therapeutic targets due to inherently high disease state specificity. Recent advances in chemical cross-linking strategies coupled with mass spectrometry (XL-MS) have positioned XL-MS as a promising technology to not only elucidate the molecular architecture of individual protein assemblies, but also to characterize proteome-wide PPI networks. Moreover, quantitative in vivo XL-MS provides a new capability for the visualization of cellular interactome dynamics elicited by drug treatments, disease states, or aging effects. The emerging field of XL-MS based complexomics enables unique insights on protein moonlighting and protein complex remodeling. These techniques provide complimentary information necessary for in-depth structural interactome studies to better comprehend how PPIs mediate function in living systems.

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

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

Structures, dynamics, complexes, and functions: From classic computation to artificial intelligence

Computational approaches can provide highly detailed insight into the molecular recognition processes that underlie drug binding, the assembly of protein complexes, and the regulation of biological functional processes. Classical simulation methods can bridge a wide range of length- and time-scales typically involved in such processes. Lately, automated learning and artificial intelligence methods have shown the potential to expand the reach of physics-based approaches, ushering in the possibility to model and even design complex protein architectures. The synergy between atomistic simulations and AI methods is an emerging frontier with a huge potential for advances in structural biology. Herein, we explore various examples and frameworks for these approaches, providing select instances and applications that illustrate their impact on fundamental biomolecular problems.

Integrative modeling meets deep learning: Recent advances in modeling protein assemblies

Recent progress in protein structure prediction based on deep learning revolutionized the field of Structural Biology. Beyond single proteins, it also enabled high-throughput prediction of structures of protein-protein interactions. Despite the success in predicting complex structures, large macromolecular assemblies still require specialized approaches. Here we describe recent advances in modeling macromolecular assemblies using integrative and hierarchical approaches. We highlight applications that predict protein-protein interactions and challenges in modeling complexes based on the interaction networks, including the prediction of complex stoichiometry and heterogeneity.

Combining Complementarity and Binding Energetics in the Assessment of Protein Interactions: EnCPdock-A Practical Manual

The combined effect of shape and electrostatic complementarities (Sc, EC) at the interface of the interacting protein partners (PPI) serves as the physical basis for such associations and is a strong determinant of their binding energetics. EnCPdock (https://www.scinetmol.in/EnCPdock/) presents a comprehensive web platform for the direct conjoint comparative analyses of complementarity and binding energetics in PPIs. It elegantly interlinks the dual nature of local (Sc) and nonlocal complementarity (EC) in PPIs using the complementarity plot. It further derives an AI-based ΔGbinding with a prediction accuracy comparable to the state of the art. This book chapter presents a practical manual to conceptualize and implement EnCPdock with its various features and functionalities, collectively having the potential to serve as a valuable protein engineering tool in the design of novel protein interfaces.

Diverse genetic conflicts mediated by molecular mimicry and computational approaches to detect them

In genetic conflicts between intergenomic and selfish elements, driver and killer elements achieve biased survival, replication, or transmission over sensitive and targeted elements through a wide range of molecular mechanisms, including mimicry. Driving mechanisms manifest at all organismal levels, from the biased propagation of individual genes, as demonstrated by transposable elements, to the biased transmission of genomes, as illustrated by viruses, to the biased transmission of cell lineages, as in cancer. Targeted genomes are vulnerable to molecular mimicry through the conserved motifs they use for their own signaling and regulation. Mimicking these motifs enables an intergenomic or selfish element to control core target processes, and can occur at the sequence, structure, or functional level. Molecular mimicry was first appreciated as an important phenomenon more than twenty years ago. Modern genomics technologies, databases, and machine learning approaches offer tremendous potential to study the distribution of molecular mimicry across genetic conflicts in nature. Here, we explore the theoretical expectations for molecular mimicry between conflicting genomes, the trends in molecular mimicry mechanisms across known genetic conflicts, and outline how new examples can be gleaned from population genomic datasets. We discuss how mimics involving short sequence-based motifs or gene duplications can evolve convergently from new mutations. Whereas, processes that involve divergent domains or fully-folded structures occur among genomes by horizontal gene transfer. These trends are largely based on a small number of organisms and should be reevaluated in a general, phylogenetically independent framework. Currently, publicly available databases can be mined for genotypes driving non-Mendelian inheritance patterns, epistatic interactions, and convergent protein structures. A subset of these conflicting elements may be molecular mimics. We propose approaches for detecting genetic conflict and molecular mimicry from these datasets.

Enhancement of Green Production of Heme by Deleting Odor-Related Genes from Bacillus amyloliquefaciens Based on CRISPR/Cas9n

Heme is a crucial component in endowing plant-based meat analogs with flavor and color. This study aimed to develop a green strategy for heme production by reducing fermentation off-odor and accelerating heme synthesis. First, an efficient CRISPR/Cas9n system was constructed in Bacillus amyloliquefaciens to construct the odor-reducing chassis cell HZC9nΔGPSU, and the odor substances including the branched-chain short fatty acids, putrescine, and ammonia were reduced by 62, 70, and 88%, respectively. Meanwhile, the hemA gene was confirmed to be the key gene for enhanced heme synthesis. Various hemA genes were compared to obtain the best gene dhemA, and the catalysis mechanism was explained by molecular docking simulation. After further expression of dhemA in HZC9nΔGPSU, the heme titer of HZC9nΔGPSU/pHY-dhemA reached 11.31 ± 0.51 mg/L, 1.70-fold higher than that of HZC9n/pHY-dhemA. The knockout of off-odor-related genes reduced the odor substances and enhanced the heme synthesis, which is promising for the green production of high-quality heme.

Unraveling the mechanism of small molecule induced activation of Staphylococcus aureus signal peptidase IB

Staphylococcus aureus signal peptidase IB (SpsB) is an essential enzyme for protein secretion. While inhibition of its activity by small molecules is a well-precedented mechanism to kill bacteria, the mode of activation is however less understood. We here investigate the activation mechanism of a recently introduced activator, the antibiotic compound PK150, and demonstrate by combined experimental and Molecular Dynamics (MD) simulation studies a unique principle of enzyme stimulation. Mass spectrometric studies with an affinity-based probe of PK150 unravel the binding site of PK150 in SpsB which is used as a starting point for MD simulations. Our model shows the localization of the molecule in an allosteric pocket next to the active site which shields the catalytic dyad from excess water that destabilizes the catalytic geometry. This mechanism is validated by the placement of mutations aligning the binding pocket of PK150. While the mutants retain turnover of the SpsB substrate, no stimulation of activity is observed upon PK150 addition. Overall, our study elucidates a previously little investigated mechanism of enzyme activation and serves as a starting point for the development of future enzyme activators.

STK19 positions TFIIH for cell-free transcription-coupled DNA repair

In transcription-coupled repair, stalled RNA polymerase II (Pol II) is recognized by CSB and CRL4CSA, which co-operate with UVSSSA and ELOF1 to recruit TFIIH for nucleotide excision repair (TC-NER). To explore the mechanism of TC-NER, we recapitulated this reaction in vitro. When a plasmid containing a site-specific lesion is transcribed in frog egg extract, error-free repair is observed that depends on CSB, CRL4CSA, UVSSA, and ELOF1. Repair also depends on STK19, a factor previously implicated in transcription recovery after UV exposure. A 1.9 Å cryo-electron microscopy structure shows that STK19 joins the TC-NER complex by binding CSA and the RPB1 subunit of Pol II. Furthermore, AlphaFold predicts that STK19 interacts with the XPD subunit of TFIIH, and disrupting this interface impairs cell-free repair. Molecular modeling suggests that STK19 positions TFIIH ahead of Pol II for lesion verification. In summary, our analysis of cell-free TC-NER suggests that STK19 couples RNA polymerase II stalling to downstream repair events.

African Swine Fever Virus Protein-Protein Interaction Prediction

The African swine fever virus (ASFV) is an often deadly disease in swine and poses a threat to swine livestock and swine producers. With its complex genome containing more than 150 coding regions, developing effective vaccines for this virus remains a challenge due to a lack of basic knowledge about viral protein function and protein-protein interactions between viral proteins and between viral and host proteins. In this work, we identified ASFV-ASFV protein-protein interactions (PPIs) using artificial intelligence-powered protein structure prediction tools. We benchmarked our PPI identification workflow on the Vaccinia virus, a widely studied nucleocytoplasmic large DNA virus, and found that it could identify gold-standard PPIs that have been validated in vitro in a genome-wide computational screening. We applied this workflow to more than 18,000 pairwise combinations of ASFV proteins and were able to identify seventeen novel PPIs, many of which have corroborating experimental or bioinformatic evidence for their protein-protein interactions, further validating their relevance. Two protein-protein interactions, I267L and I8L, I267L__I8L, and B175L and DP79L, B175L__DP79L, are novel PPIs involving viral proteins known to modulate host immune response.

Multiscale photocatalytic proximity labeling reveals cell surface neighbors on and between cells

Proximity labeling proteomics (PLP) strategies are powerful approaches to yield snapshots of protein neighborhoods. Here, we describe a multiscale PLP method with adjustable resolution that uses a commercially available photocatalyst, Eosin Y, which upon visible light illumination activates different photo-probes with a range of labeling radii. We applied this platform to profile neighborhoods of the oncogenic epidermal growth factor receptor and orthogonally validated more than 20 neighbors using immunoassays and AlphaFold-Multimer prediction. We further profiled the protein neighborhoods of cell-cell synapses induced by bispecific T cell engagers and chimeric antigen receptor T cells. This integrated multiscale PLP platform maps local and distal protein networks on and between cell surfaces, which will aid in the systematic construction of the cell surface interactome, revealing horizontal signaling partners and reveal new immunotherapeutic opportunities.

Engineered reversible inhibition of SpyCatcher reactivity enables rapid generation of bispecific antibodies

The precise regulation of protein function is essential in biological systems and a key goal in chemical biology and protein engineering. Here, we describe a straightforward method to engineer functional control into the isopeptide bond-forming SpyTag/SpyCatcher protein ligation system. First, we perform a cysteine scan of the structured region of SpyCatcher. Except for two known reactive and catalytic residues, none of these mutations abolish reactivity. In a second screening step, we modify the cysteines with disulfide bond-forming small molecules. Here we identify 8 positions at which modifications strongly inhibit reactivity. This inhibition can be reversed by reducing agents. We call such a reversibly inhibitable SpyCatcher "SpyLock". Using "BiLockCatcher", a genetic fusion of wild-type SpyCatcher and SpyLock, and SpyTagged antibody fragments, we generate bispecific antibodies in a single, scalable format, facilitating the screening of a large number of antibody combinations. We demonstrate this approach by screening anti-PD-1/anti-PD-L1 bispecific antibodies using a cellular reporter assay.

Comparative genomic analysis of nickel homeostasis in cable bacteria

BACKGROUND

Cable bacteria are filamentous members of the Desulfobulbaceae family that are capable of performing centimetre‑scale electron transport in marine and freshwater sediments. This long‑distance electron transport is mediated by a network of parallel conductive fibres embedded in the cell envelope. This fibre network efficiently transports electrical currents along the entire length of the centimetre‑long filament. Recent analyses show that these fibres consist of metalloproteins that harbour a novel nickel‑containing cofactor, which indicates that cable bacteria have evolved a unique form of biological electron transport. This nickel‑dependent conduction mechanism suggests that cable bacteria are strongly dependent on nickel as a biosynthetic resource. Here, we performed a comprehensive comparative genomic analysis of the genes linked to nickel homeostasis. We compared the genome‑encoded adaptation to nickel of cable bacteria to related members of the Desulfobulbaceae family and other members of the Desulfobulbales order.

RESULTS

Presently, four closed genomes are available for the monophyletic cable bacteria clade that consists of the genera Candidatus Electrothrix and Candidatus Electronema. To increase the phylogenomic coverage, we additionally generated two closed genomes of cable bacteria: Candidatus Electrothrix gigas strain HY10‑6 and Candidatus Electrothrix antwerpensis strain GW3‑4, which are the first closed genomes of their respective species. Nickel homeostasis genes were identified in a database of 38 cable bacteria genomes (including 6 closed genomes). Gene prevalence was compared to 19 genomes of related strains, residing within the Desulfobulbales order but outside of the cable bacteria clade, revealing several genome‑encoded adaptations to nickel homeostasis in cable bacteria. Phylogenetic analysis indicates that nickel importers, nickel‑binding enzymes and nickel chaperones of cable bacteria are affiliated to organisms outside the Desulfobulbaceae family, with several proteins showing affiliation to organisms outside of the Desulfobacterota phylum. Conspicuously, cable bacteria encode a unique periplasmic nickel export protein RcnA, which possesses a putative cytoplasmic histidine‑rich loop that has been largely expanded compared to RcnA homologs in other organisms.

CONCLUSION

Cable bacteria genomes show a clear genetic adaptation for nickel utilization when compared to closely related genera. This fully aligns with the nickel‑dependent conduction mechanism that is uniquely found in cable bacteria.

MGPPI: multiscale graph neural networks for explainable protein-protein interaction prediction

Protein-Protein Interactions (PPIs) involves in various biological processes, which are of significant importance in cancer diagnosis and drug development. Computational based PPI prediction methods are more preferred due to their low cost and high accuracy. However, existing protein structure based methods are insufficient in the extraction of protein structural information. Furthermore, most methods are less interpretable, which hinder their practical application in the biomedical field. In this paper, we propose MGPPI, which is a Multiscale graph convolutional neural network model for PPI prediction. By incorporating multiscale module into the Graph Neural Network (GNN) and constructing multi convolutional layers, MGPPI can effectively capture both local and global protein structure information. For model interpretability, we introduce a novel visual explanation method named Gradient Weighted interaction Activation Mapping (Grad-WAM), which can highlight key binding residue sites. We evaluate the performance of MGPPI by comparing with state-of-the-arts methods on various datasets. Results shows that MGPPI outperforms other methods significantly and exhibits strong generalization capabilities on the multi-species dataset. As a practical case study, we predicted the binding affinity between the spike (S) protein of SARS-COV-2 and the human ACE2 receptor protein, and successfully identified key binding sites with known binding functions. Key binding sites mutation in PPIs can affect cancer patient survival statues. Therefore, we further verified Grad-WAM highlighted residue sites in separating patients survival groups in several different cancer type datasets. According to our results, some of the highlighted residues can be used as biomarkers in predicting patients survival probability. All these results together demonstrate the high accuracy and practical application value of MGPPI. Our method not only addresses the limitations of existing approaches but also can assists researchers in identifying crucial drug targets and help guide personalized cancer treatment.

GET: a foundation model of transcription across human cell types

Transcriptional regulation, involving the complex interplay between regulatory sequences and proteins, directs all biological processes. Computational models of transcription lack generalizability to accurately extrapolate in unseen cell types and conditions. Here, we introduce GET, an interpretable foundation model designed to uncover regulatory grammars across 213 human fetal and adult cell types. Relying exclusively on chromatin accessibility data and sequence information, GET achieves experimental-level accuracy in predicting gene expression even in previously unseen cell types. GET showcases remarkable adaptability across new sequencing platforms and assays, enabling regulatory inference across a broad range of cell types and conditions, and uncovering universal and cell type specific transcription factor interaction networks. We evaluated its performance on prediction of regulatory activity, inference of regulatory elements and regulators, and identification of physical interactions between transcription factors. Specifically, we show GET outperforms current models in predicting lentivirus-based massive parallel reporter assay readout with reduced input data. In fetal erythroblasts, we identify distal (>1Mbp) regulatory regions that were missed by previous models. In B cells, we identified a lymphocyte-specific transcription factor-transcription factor interaction that explains the functional significance of a leukemia-risk predisposing germline mutation. In sum, we provide a generalizable and accurate model for transcription together with catalogs of gene regulation and transcription factor interactions, all with cell type specificity.

Pairing interacting protein sequences using masked language modeling

Predicting which proteins interact together from amino acid sequences is an important task. We develop a method to pair interacting protein sequences which leverages the power of protein language models trained on multiple sequence alignments (MSAs), such as MSA Transformer and the EvoFormer module of AlphaFold. We formulate the problem of pairing interacting partners among the paralogs of two protein families in a differentiable way. We introduce a method called Differentiable Pairing using Alignment-based Language Models (DiffPALM) that solves it by exploiting the ability of MSA Transformer to fill in masked amino acids in multiple sequence alignments using the surrounding context. MSA Transformer encodes coevolution between functionally or structurally coupled amino acids within protein chains. It also captures inter-chain coevolution, despite being trained on single-chain data. Relying on MSA Transformer without fine-tuning, DiffPALM outperforms existing coevolution-based pairing methods on difficult benchmarks of shallow multiple sequence alignments extracted from ubiquitous prokaryotic protein datasets. It also outperforms an alternative method based on a state-of-the-art protein language model trained on single sequences. Paired alignments of interacting protein sequences are a crucial ingredient of supervised deep learning methods to predict the three-dimensional structure of protein complexes. Starting from sequences paired by DiffPALM substantially improves the structure prediction of some eukaryotic protein complexes by AlphaFold-Multimer. It also achieves competitive performance with using orthology-based pairing.

APACE: AlphaFold2 and advanced computing as a service for accelerated discovery in biophysics

The prediction of protein 3D structure from amino acid sequence is a computational grand challenge in biophysics and plays a key role in robust protein structure prediction algorithms, from drug discovery to genome interpretation. The advent of AI models, such as AlphaFold, is revolutionizing applications that depend on robust protein structure prediction algorithms. To maximize the impact, and ease the usability, of these AI tools we introduce APACE, AlphaFold2 and advanced computing as a service, a computational framework that effectively handles this AI model and its TB-size database to conduct accelerated protein structure prediction analyses in modern supercomputing environments. We deployed APACE in the Delta and Polaris supercomputers and quantified its performance for accurate protein structure predictions using four exemplar proteins: 6AWO, 6OAN, 7MEZ, and 6D6U. Using up to 300 ensembles, distributed across 200 NVIDIA A100 GPUs, we found that APACE is up to two orders of magnitude faster than off-the-self AlphaFold2 implementations, reducing time-to-solution from weeks to minutes. This computational approach may be readily linked with robotics laboratories to automate and accelerate scientific discovery.

AlphaFold2 as a replacement for solution NMR structure determination of small proteins: Not so fast!

The determination of a protein's structure is often a first step towards the development of a mechanistic understanding of its function. Considerable advances in computational protein structure prediction have been made in recent years, with AlphaFold2 (AF2) emerging as the primary tool used by researchers for this purpose. While AF2 generally predicts accurate structures of folded proteins, we present here a case where AF2 incorrectly predicts the structure of a small, folded and compact protein with high confidence. This protein, pro-interleukin-18 (pro-IL-18), is the precursor of the cytokine IL-18. Interestingly, the structure of pro-IL-18 predicted by AF2 matches that of the mature cytokine, and not the corresponding experimentally determined structure of the pro-form of the protein. Thus, while computational structure prediction holds immense promise for addressing problems in protein biophysics, there is still a need for experimental structure determination, even in the context of small well-folded, globular proteins.

Antigen specific VNAR screening in whitespotted bamboo shark (Chiloscyllium plagiosum) with next generation sequencing

IgNAR exhibits significant promise in the fields of cancer and anti-virus biotherapies. Notably, the variable regions of IgNAR (VNAR) possess comparable antigen binding affinity with much smaller molecular weight (∼12 kDa) compared to IgNAR. Antigen specific VNAR screening is a changeling work, which limits its application in medicine and therapy fields. Though phage display is a powerful tool for VNAR screening, it has a lot of drawbacks, such as small library coverage, low expression levels, unstable target protein, complicating and time-consuming procedures. Here we report VANR screening with next generation sequencing (NGS) could effectively overcome the limitations of phage display, and we successfully identified approximately 3000 BAFF-specific VNARs in Chiloscyllium plagiosum vaccinated with the BAFF antigen. The results of modelling and molecular dynamics simulation and ELISA assay demonstrated that one out of the top five abundant specific VNARs exhibited higher binding affinity to the BAFF antigen than those obtained through phage display screening. Our data indicates NGS would be an alternative way for VNAR screening with plenty of advantages.

LazyAF, a pipeline for accessible medium-scale in silico prediction of protein-protein interactions

Artificial intelligence has revolutionized the field of protein structure prediction. However, with more powerful and complex software being developed, it is accessibility and ease of use rather than capability that is quickly becoming a limiting factor to end users. LazyAF is a Google Colaboratory-based pipeline which integrates the existing ColabFold BATCH software to streamline the process of medium-scale protein-protein interaction prediction. LazyAF was used to predict the interactome of the 76 proteins encoded on the broad-host-range multi-drug resistance plasmid RK2, demonstrating the ease and accessibility the pipeline provides.

High-Speed Atomic Force Microscopy for Filming Protein Molecules in Dynamic Action

Structural biology is currently undergoing a transformation into dynamic structural biology, which reveals the dynamic structure of proteins during their functional activity to better elucidate how they function. Among the various approaches in dynamic structural biology, high-speed atomic force microscopy (HS-AFM) is unique in the ability to film individual molecules in dynamic action, although only topographical information is acquirable. This review provides a guide to the use of HS-AFM for biomolecular imaging and showcases several examples, as well as providing information on up-to-date progress in HS-AFM technology. Finally, we discuss the future prospects of HS-AFM in the context of dynamic structural biology in the upcoming era.

AlphaFold2 structures guide prospective ligand discovery

AlphaFold2 (AF2) models have had wide impact but mixed success in retrospective ligand recognition. We prospectively docked large libraries against unrefined AF2 models of the σ2 and serotonin 2A (5-HT2A) receptors, testing hundreds of new molecules and comparing results with those obtained from docking against the experimental structures. Hit rates were high and similar for the experimental and AF2 structures, as were affinities. Success in docking against the AF2 models was achieved despite differences between orthosteric residue conformations in the AF2 models and the experimental structures. Determination of the cryo-electron microscopy structure for one of the more potent 5-HT2A ligands from the AF2 docking revealed residue accommodations that resembled the AF2 prediction. AF2 models may sample conformations that differ from experimental structures but remain low energy and relevant for ligand discovery, extending the domain of structure-based drug design.

Molecular insights into the activation of Mre11-Rad50 endonuclease activity by Sae2/CtIP

In Saccharomyces cerevisiae (S. cerevisiae), Mre11-Rad50-Xrs2 (MRX)-Sae2 nuclease activity is required for the resection of DNA breaks with secondary structures or protein blocks, while in humans, the MRE11-RAD50-NBS1 (MRN) homolog with CtIP is needed to initiate DNA end resection of all breaks. Phosphorylated Sae2/CtIP stimulates the endonuclease activity of MRX/N. Structural insights into the activation of the Mre11 nuclease are available only for organisms lacking Sae2/CtIP, so little is known about how Sae2/CtIP activates the nuclease ensemble. Here, we uncover the mechanism of Mre11 activation by Sae2 using a combination of AlphaFold2 structural modeling of biochemical and genetic assays. We show that Sae2 stabilizes the Mre11 nuclease in a conformation poised to cleave substrate DNA. Several designs of compensatory mutations establish how Sae2 activates MRX in vitro and in vivo, supporting the structural model. Finally, our study uncovers how human CtIP, despite considerable sequence divergence, employs a similar mechanism to activate MRN.

Predicting human and viral protein variants affecting COVID-19 susceptibility and repurposing therapeutics

The COVID-19 disease is an ongoing global health concern. Although vaccination provides some protection, people are still susceptible to re-infection. Ostensibly, certain populations or clinical groups may be more vulnerable. Factors causing these differences are unclear and whilst socioeconomic and cultural differences are likely to be important, human genetic factors could influence susceptibility. Experimental studies indicate SARS-CoV-2 uses innate immune suppression as a strategy to speed-up entry and replication into the host cell. Therefore, it is necessary to understand the impact of variants in immunity-associated human proteins on susceptibility to COVID-19. In this work, we analysed missense coding variants in several SARS-CoV-2 proteins and their human protein interactors that could enhance binding affinity to SARS-CoV-2. We curated a dataset of 19 SARS-CoV-2: human protein 3D-complexes, from the experimentally determined structures in the Protein Data Bank and models built using AlphaFold2-multimer, and analysed the impact of missense variants occurring in the protein-protein interface region. We analysed 468 missense variants from human proteins and 212 variants from SARS-CoV-2 proteins and computationally predicted their impacts on binding affinities for the human viral protein complexes. We predicted a total of 26 affinity-enhancing variants from 13 human proteins implicated in increased binding affinity to SARS-CoV-2. These include key-immunity associated genes (TOMM70, ISG15, IFIH1, IFIT2, RPS3, PALS1, NUP98, AXL, ARF6, TRIMM, TRIM25) as well as important spike receptors (KREMEN1, AXL and ACE2). We report both common (e.g., Y13N in IFIH1) and rare variants in these proteins and discuss their likely structural and functional impact, using information on known and predicted functional sites. Potential mechanisms associated with immune suppression implicated by these variants are discussed. Occurrence of certain predicted affinity-enhancing variants should be monitored as they could lead to increased susceptibility and reduced immune response to SARS-CoV-2 infection in individuals/populations carrying them. Our analyses aid in understanding the potential impact of genetic variation in immunity-associated proteins on COVID-19 susceptibility and help guide drug-repurposing strategies.

Elucidating key determinants of engineered scFv antibody in MMP-9 binding using high throughput screening and machine learning

An imbalance in matrix metalloproteinase-9 (MMP-9) regulation can lead to numerous diseases, including neurological disorders, cancer, and pre-term labor. Engineering single-chain antibody fragments (scFvs) Targeting MMP-9 to develop novel therapeutics for such diseases is desirable. We screened a synthetic scFv antibody library displayed on the yeast surface for binding improvement to MMP-9 using FACS (fluorescent-activated cell sorting). The scFv antibody clones isolated after FACS showed improvement in binding to MMP-9 compared to the endogenous inhibitor. To understand molecular determinants of binding between engineered scFv antibody variants and MMP-9, next-generation DNA sequencing, and computational protein structure analysis were used. Additionally, a deep-learning language model was trained on the synthetic library to predict the binding of scFv variants using their CDR-H3 sequences.

Identification of a Novel Chitinase from Bacillus paralicheniformis: Gene Mining, Sequence Analysis, and Enzymatic Characterization

In this study, a novel strain for degrading chitin was identified as Bacillus paralicheniformis HL37, and the key chitinase CH1 was firstly mined through recombinant expression in Bacillus amyloliquefaciens HZ12. Subsequently, the sequence composition and catalytic mechanism of CH1 protein were analyzed. The molecular docking indicated that the triplet of Asp526, Asp528, and Glu530 was a catalytic active center. The enzymatic properties analysis revealed that the optimal reaction temperature and pH was 65 °C and 6.0, respectively. Especially, the chitinase activity showed no significant change below 55 °C and it could maintain over 60% activity after exposure to 85 °C for 30 min. Moreover, the optimal host strain and signal peptide were obtained to enhance the expression of chitinase CH1 significantly. As far as we know, it was the first time finding the highly efficient chitin-degrading enzymes in B. paralicheniformis, and detailed explanations were provided on the catalytic mechanism and enzymatic properties on CH1.