AI revolutions in biology: The joys and perils of AlphaFold

Advancements in protein structure prediction: A comparative overview of AlphaFold and its derivatives

This review provides a comprehensive analysis of AlphaFold (AF) and its derivatives (AF2 and AF3) in protein structure prediction. These tools have revolutionized structural biology with their highly accurate predictions, driving progress in protein modeling, drug discovery, and the study of protein dynamics. Its exceptional accuracy has redefined our understanding of protein folding, which enables groundbreaking advancements in protein design, disease research and discusses future integration with experimental techniques. In addition, their achievement features, architectures, important case studies, and noteworthy effects in the field of biology and medicine were evaluated. In consideration of the fact that AF2 is a relatively recent innovation, it has already been taken into account in many studies that highlight its applications in many ways. Moreover, the limitations of AF2 that directed to the introduction of AF3 are also reported, which is a great improvement as it provides precise predictions of the structures and interactions of proteins, DNA, RNA, and ligands, thereby aiding in the understanding of the molecular level. Addressing current challenges and forecasting future developments, this work underscores the lasting significance of AF in reshaping the scientific landscape of protein research.

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

INTRODUCTION

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

AREAS COVERED

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

EXPERT OPINION

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

Decoding transaminase motifs: Tracing the unknown patterns for enhancing the accuracy of computational screening methodologies

Transaminases, enzymes known for their amino group transfer capabilities, encompass four distinct subfamilies: D-alanine transaminase (DATA), L-selective Branched chain aminotransferase (BCAT), and 4-amino-4-deoxychorismate lyase (ADCL) and R-selective aminotransferase (RATA). RATA enzymes are particularly valuable in biocatalysis for synthesizing chiral amines and resolving racemic mixtures, yet their identification in sequence databases is challenging due to the lack of robust motif-based screening methods. Constructing a sequence dataset of transaminases, and categorizing them to various subfamilies, the conserved motifs are screened over the experimentally known ones, and the novel motifs are explored. Phylogenetic clustering of these subfamilies and structural localization of the identified motifs on the Alphafold-predicted protein models of the representative sequences validate their functional importance. For the ADCL, BCAT, DATA, and RATA datasets, we identified 5, 7, 10, and 2 novel motifs, with 3, 5, 7, and 2 motifs localized on secondary structures, confirming their structural importance. Furthermore, the analysis revealed 1, 3, 2, and 1 unique residue patterns of 293-KxxxR-297; 336-KxxxxY-341, 379-ExxxxNxF-386, and 453-ExFxxGT-459; 187-HxxRL-191, and 284-DxRWxxCDIK-293; and 191-HxxRL-195, integrating of which in the known computational tools would improve their accuracy. The conserved residue pattern or motif-based computational approach for robustly screening the transaminases holds promise for unveiling the novel RATA enzymes, facilitating their exploitation in biocatalytic applications.

Predicting the impact of missense mutations on an unresolved protein's stability, structure, and function: A case study of Alzheimer's disease-associated TREM2 R47H variant

AlphaFold2 (AF2) has spurred a revolution in predicting unresolved structures of wild-type proteins with high accuracy. However, AF2 falls short of predicting the effects of missense mutations on unresolved protein structures that may be informative to efforts in personalized medicine. Over the last decade, countless in-silico methods have been developed to predict the pathogenicity of point mutations on resolved structures, but no studies have evaluated their capabilities on unresolved protein structures predicted by AF2. Herein, we investigated Alzheimer's disease (AD)-causing coding variants of the triggering receptor expressed on myeloid cells 2 (TREM2) receptor using in-silico mutagenesis techniques on the AF2-predicted structure. We first demonstrated that the predicted structure retained a high accuracy in critical regions of the extracellular domain and subsequently validated the in-silico mutagenesis methods by evaluating the effects of the strongest risk variant R47H of TREM2. After validation of the R47H variant, we predicted the molecular basis and effects on protein stability and ligand-binding affinity of the R62H and D87N variants that remain unknown in current literature. By comparing it with the R47H variant, our analysis reveals that R62H and D87N variants exert a much less pronounced effect on the structural stability of TREM2. These in-silico findings show the possibility that the R62H and D87N mutations are likely less pathogenic than the R47H AD. Lastly, we investigated the Nasu-Hakola (NHD)-causing Y38C and V126G TREM2 as a comparison and found that they imposed greater destabilization compared to AD-causing variants. We believe that the in-silico mutagenesis methods described here can be applied broadly to evaluate the ever-growing numbers of protein mutations/variants discovered in human genetics study for their potential in diseases, ultimately facilitating personalized medicine.

Bioinformatics and Computationally Supported Redesign of Aspartase for β-Alanine Synthesis by Acrylic Acid Hydroamination

Aspartate ammonia lyases catalyze the reversible amination of fumarate to l-aspartate. Recent studies demonstrate that the thermostable enzyme from Bacillus sp. YM55-1 (AspB) can be engineered for the enantioselective production of substituted β-amino acids. This reaction would be attractive for the conversion of acrylic acid to β-alanine, which is an important building block for the preparation of bioactive compounds. Here we describe a bioinformatics and computational approach aimed at introducing the β-alanine synthesis activity. Three strategies were used: First, we redesigned the α-carboxylate binding pocket of AspB to introduce activity with the acrylic acid. Next, different template enzymes were identified by genome mining, equipped with a redesigned α-carboxylate pocket, and investigated for β-alanine synthesis, which yielded variants with better activity. Third, interactions of the SS-loop that covers the active site and harbors a catalytic serine were computationally redesigned using energy calculations to stabilize reactive conformations and thereby further increase the desired β-alanine synthesis activity. Different improved enzymes were obtained and the best variants showed k cat values with acrylic acid of at least 0.6-1.5 s-1 with K M values in the high mM range. Since the β-alanine production of wild-type enzyme was below the detection limit, this suggests that the k cat/K m was improved by at least 1000-fold. Crystal structures of the 6-fold mutant of redesigned AspB and the similarly engineered aspartase from Caenibacillus caldisaponilyticus revealed that their ligand-free structures have the SS-loop in a closed (reactive) conformation, which for wild-type AspB is only observed in the substrate-bound enzyme. AlphaFold-generated models suggest that other aspartase variants redesigned for acrylic acid hydroamination also prefer a 3D structure with the loop in a closed conformation. The combination of binding pocket redesign, genome mining, and enhanced active-site loop closure thus created effective β-alanine synthesizing variants of aspartase.

Lessons from Deep Learning Structural Prediction of Multistate Multidomain Proteins-The Case Study of Coiled-Coil NOD-like Receptors

We test here the prediction capabilities of the new generation of deep learning predictors in the more challenging situation of multistate multidomain proteins by using as a case study a coiled-coil family of Nucleotide-binding Oligomerization Domain-like (NOD-like) receptors from A. thaliana and a few extra examples for reference. Results reveal a truly remarkable ability of these platforms to correctly predict the 3D structure of modules that fold in well-established topologies. A lower performance is noticed in modeling morphing regions of these proteins, such as the coiled coils. Predictors also display a good sensitivity to local sequence drifts upon the modeling solution of the overall modular configuration. In multivalued 1D to 3D mappings, the platforms display a marked tendency to model proteins in the most compact configuration and must be retrained by information filtering to drive modeling toward the sparser ones. Bias toward order and compactness is seen at the secondary structure level as well. All in all, using AI predictors for modeling multidomain multistate proteins when global templates are at hand is fruitful, but the above challenges have to be taken into account. In the absence of global templates, a piecewise modeling approach with experimentally constrained reconstruction of the global architecture might give more realistic results.

The role of artificial intelligence in pandemic responses: from epidemiological modeling to vaccine development

Integrating Artificial Intelligence (AI) across numerous disciplines has transformed the worldwide landscape of pandemic response. This review investigates the multidimensional role of AI in the pandemic, which arises as a global health crisis, and its role in preparedness and responses, ranging from enhanced epidemiological modelling to the acceleration of vaccine development. The confluence of AI technologies has guided us in a new era of data-driven decision-making, revolutionizing our ability to anticipate, mitigate, and treat infectious illnesses. The review begins by discussing the impact of a pandemic on emerging countries worldwide, elaborating on the critical significance of AI in epidemiological modelling, bringing data-driven decision-making, and enabling forecasting, mitigation and response to the pandemic. In epidemiology, AI-driven epidemiological models like SIR (Susceptible-Infectious-Recovered) and SIS (Susceptible-Infectious-Susceptible) are applied to predict the spread of disease, preventing outbreaks and optimising vaccine distribution. The review also demonstrates how Machine Learning (ML) algorithms and predictive analytics improve our knowledge of disease propagation patterns. The collaborative aspect of AI in vaccine discovery and clinical trials of various vaccines is emphasised, focusing on constructing AI-powered surveillance networks. Conclusively, the review presents a comprehensive assessment of how AI impacts epidemiological modelling, builds AI-enabled dynamic models by collaborating ML and Deep Learning (DL) techniques, and develops and implements vaccines and clinical trials. The review also focuses on screening, forecasting, contact tracing and monitoring the virus-causing pandemic. It advocates for sustained research, real-world implications, ethical application and strategic integration of AI technologies to strengthen our collective ability to face and alleviate the effects of global health issues.

Global atlas of predicted functional domains in Legionella pneumophila Dot/Icm translocated effectors

Legionella pneumophila utilizes the Dot/Icm type IVB secretion system to deliver hundreds of effector proteins inside eukaryotic cells to ensure intracellular replication. Our understanding of the molecular functions of the largest pathogenic arsenal known to the bacterial world remains incomplete. By leveraging advancements in 3D protein structure prediction, we provide a comprehensive structural analysis of 368 L. pneumophila effectors, representing a global atlas of predicted functional domains summarized in a database ( https://pathogens3d.org/legionella-pneumophila ). Our analysis identified 157 types of diverse functional domains in 287 effectors, including 159 effectors with no prior functional annotations. Furthermore, we identified 35 cryptic domains in 30 effector models that have no similarity with experimentally structurally characterized proteins, thus, hinting at novel functionalities. Using this analysis, we demonstrate the activity of thirteen functional domains, including three cryptic domains, predicted in L. pneumophila effectors to cause growth defects in the Saccharomyces cerevisiae model system. This illustrates an emerging strategy of exploring synergies between predictions and targeted experimental approaches in elucidating novel effector activities involved in infection.

Solving the protein folding problem…

The protein folding problem was, to paraphrase Churchill, 'A riddle wrapped in a mystery inside an enigma'. The riddle, in this context, was the folding code; what interactions at the amino acid level are driving the folding process? The mystery was the kinetic question (Levinthal's paradox); how does the folding process occur so quickly (typically in timescales ranging from μS to mS)? Finally, the enigma represents the computational problem of developing approaches to predict the final folded sate of a protein given only its amino acid sequence. Herein, I trace the path to solving this riddle wrapped in a mystery inside an enigma.

Perspectives on the future of ecology, evolution, and biodiversity from the Council on Microbial Sciences of the American Society for Microbiology

The field of microbial ecology, evolution, and biodiversity (EEB) is at the leading edge of understanding how microbes shape our biosphere and influence the well-being of humankind and Earth. To that end, EEB is developing new transdisciplinary tools to analyze these ecologically critical, complex microbial communities. The American Society for Microbiology's Council on Microbial Sciences hosted a virtual retreat in 2023 to discuss the trajectory of EEB both within the Society and microbiology writ large. The retreat emphasized the interconnectedness of microbes and their outsized global influence on environmental and host health. The maximal potential impact of EEB will not be achieved without contributions from disparate fields that unite diverse technologies and data sets. In turn, this level of transdisciplinary efforts requires actively encouraging "broad" research, spanning inclusive global collaborations that incorporate both scientists and the public. Together, the American Society for Microbiology and EEB are poised to lead a paradigm shift that will result in a new era of collaboration, innovation, and societal relevance for microbiology.

An AI-informed NMR structure reveals an extraordinary LETM1 F-EF-hand domain that functions as a two-way regulator of mitochondrial calcium

AlphaFold can accurately predict static protein structures but does not account for solvent conditions. Human leucine zipper EF-hand transmembrane protein-1 (LETM1) has one sequence-identifiable EF-hand but how calcium (Ca2+) affects structure and function remains enigmatic. Here, we used highly confident AlphaFold Cα predictions to guide nuclear Overhauser effect (NOE) assignments and structure calculation of the LETM1 EF-hand in the presence of Ca2+. The resultant NMR structure exposes pairing between a partial loop-helix and full helix-loop-helix, forming an unprecedented F-EF-hand with non-canonical Ca2+ coordination but enhanced hydrophobicity for protein interactions compared to calmodulin. The structure also reveals the basis for pH sensing at the link between canonical and partial EF-hands. Functionally, mutations that augmented or weakened Ca2+ binding increased or decreased matrix Ca2+, respectively, establishing F-EF as a two-way mitochondrial Ca2+ regulator. Thus, we show how to synergize AI prediction with NMR data, elucidating a solution-specific and extraordinary LETM1 F-EF-hand.

Computational strategies in Klebsiella pneumoniae vaccine design: navigating the landscape of in silico insights

The emergence of multidrug-resistant Klebsiella pneumoniae poses a grave threat to global public health, necessitating urgent strategies for vaccine development. In this context, computational tools have emerged as indispensable assets, offering unprecedented insights into klebsiellal biology and facilitating the design of effective vaccines. Here, a review of the application of computational methods in the development of K. pneumoniae vaccines is presented, elucidating the transformative impact of in silico approaches. Through a systematic exploration of bioinformatics, structural biology, and immunoinformatics techniques, the complex landscape of K. pneumoniae pathogenesis and antigenicity was unravelled. Key insights into virulence factors, antigen discovery, and immune response mechanisms are discussed, highlighting the pivotal role of computational tools in accelerating vaccine development efforts. Advancements in epitope prediction, antigen selection, and vaccine design optimisation are examined, highlighting the potential of in silico approaches to update vaccine development pipelines. Furthermore, challenges and future directions in leveraging computational tools to combat K. pneumoniae are discussed, emphasizing the importance of multidisciplinary collaboration and data integration. This review provides a comprehensive overview of the current state of computational contributions to K. pneumoniae vaccine development, offering insights into innovative strategies for addressing this urgent global health challenge.

The HADDOCK2.4 web server for integrative modeling of biomolecular complexes

Interactions between macromolecules, such as proteins and nucleic acids, are essential for cellular functions. Experimental methods can fail to provide all the information required to fully model biomolecular complexes at atomic resolution, particularly for large and heterogeneous assemblies. Integrative computational approaches have, therefore, gained popularity, complementing traditional experimental methods in structural biology. Here, we introduce HADDOCK2.4, an integrative modeling platform, and its updated web interface ( https://wenmr.science.uu.nl/haddock2.4 ). The platform seamlessly integrates diverse experimental and theoretical data to generate high-quality models of macromolecular complexes. The user-friendly web server offers automated parameter settings, access to distributed computing resources, and pre- and post-processing steps that enhance the user experience. To present the web server's various interfaces and features, we demonstrate two different applications: (i) we predict the structure of an antibody-antigen complex by using NMR data for the antigen and knowledge of the hypervariable loops for the antibody, and (ii) we perform coarse-grained modeling of PRC1 with a nucleosome particle guided by mutagenesis and functional data. The described protocols require some basic familiarity with molecular modeling and the Linux command shell. This new version of our widely used HADDOCK web server allows structural biologists and non-experts to explore intricate macromolecular assemblies encompassing various molecule types.

Generating Protein Structures for Pathway Discovery Using Deep Learning

Resolving the intricate details of biological phenomena at the molecular level is fundamentally limited by both length- and time scales that can be probed experimentally. Molecular dynamics (MD) simulations at various scales are powerful tools frequently employed to offer valuable biological insights beyond experimental resolution. However, while it is relatively simple to observe long-lived, stable configurations of, for example, proteins, at the required spatial resolution, simulating the more interesting rare transitions between such states often takes orders of magnitude longer than what is feasible even on the largest supercomputers available today. One common aspect of this challenge is pathway discovery, where the start and end states of a scientific phenomenon are known or can be approximated, but the mechanistic details in between are unknown. Here, we propose a representation-learning-based solution that uses interpolation and extrapolation in an abstract representation space to synthesize potential transition states, which are automatically validated using MD simulations. The new simulations of the synthesized transition states are subsequently incorporated into the representation learning, leading to an iterative framework for targeted path sampling. Our approach is demonstrated by recovering the transition of a RAS-RAF protein domain (CRD) from membrane-free to interacting with the membrane using coarse-grain MD simulations.

Unmasking AlphaFold to integrate experiments and predictions in multimeric complexes

Since the release of AlphaFold, researchers have actively refined its predictions and attempted to integrate it into existing pipelines for determining protein structures. These efforts have introduced a number of functionalities and optimisations at the latest Critical Assessment of protein Structure Prediction edition (CASP15), resulting in a marked improvement in the prediction of multimeric protein structures. However, AlphaFold's capability of predicting large protein complexes is still limited and integrating experimental data in the prediction pipeline is not straightforward. In this study, we introduce AF_unmasked to overcome these limitations. Our results demonstrate that AF_unmasked can integrate experimental information to build larger or hard to predict protein assemblies with high confidence. The resulting predictions can help interpret and augment experimental data. This approach generates high quality (DockQ score > 0.8) structures even when little to no evolutionary information is available and imperfect experimental structures are used as a starting point. AF_unmasked is developed and optimised to fill incomplete experimental structures (structural inpainting), which may provide insights into protein dynamics. In summary, AF_unmasked provides an easy-to-use method that efficiently integrates experiments to predict large protein complexes more confidently.

Insights into AlphaFold's breakthrough in neurodegenerative diseases

Neurodegenerative diseases (ND) are disorders of the central nervous system (CNS) characterized by impairment in neurons' functions, and complete loss, leading to memory loss, and difficulty in learning, language, and movement processes. The most common among these NDs are Alzheimer's disease (AD) and Parkinson's disease (PD), although several other disorders also exist. These are frontotemporal dementia (FTD), amyotrophic lateral syndrome (ALS), Huntington's disease (HD), and others; the major pathological hallmark of NDs is the proteinopathies, either of amyloid-β (Aβ), tauopathies, or synucleinopathies. Aggregation of proteins that do not undergo normal configuration, either due to mutations or through some disturbance in cellular pathway contributes to the diseases. Artificial Intelligence (AI) and deep learning (DL) have proven to be successful in the diagnosis and treatment of various congenital diseases. DL approaches like AlphaFold (AF) are a major leap towards success in CNS disorders. This 3D protein geometry modeling algorithm developed by DeepMind has the potential to revolutionize biology. AF has the potential to predict 3D-protein confirmation at an accuracy level comparable to experimentally predicted one, with the additional advantage of precisely estimating protein interactions. This breakthrough will be beneficial to identify diseases' advancement and the disturbance of signaling pathways stimulating impaired functions of proteins. Though AlphaFold has solved a major problem in structural biology, it cannot predict membrane proteins-a beneficial approach for drug designing.

Utilizing anomalous signals for element identification in macromolecular crystallography

AlphaFold2 has revolutionized structural biology by offering unparalleled accuracy in predicting protein structures. Traditional methods for determining protein structures, such as X-ray crystallography and cryo-electron microscopy, are often time-consuming and resource-intensive. AlphaFold2 provides models that are valuable for molecular replacement, aiding in model building and docking into electron density or potential maps. However, despite its capabilities, models from AlphaFold2 do not consistently match the accuracy of experimentally determined structures, need to be validated experimentally and currently miss some crucial information, such as post-translational modifications, ligands and bound ions. In this paper, the advantages are explored of collecting X-ray anomalous data to identify chemical elements, such as metal ions, which are key to understanding certain structures and functions of proteins. This is achieved through methods such as calculating anomalous difference Fourier maps or refining the imaginary component of the anomalous scattering factor f''. Anomalous data can serve as a valuable complement to the information provided by AlphaFold2 models and this is particularly significant in elucidating the roles of metal ions.

Exploring the Artificial Intelligence and Its Impact in Pharmaceutical Sciences: Insights Toward the Horizons Where Technology Meets Tradition

The technological revolutions in computers and the advancement of high-throughput screening technologies have driven the application of artificial intelligence (AI) for faster discovery of drug molecules with more efficiency, and cost-friendly finding of hit or lead molecules. The ability of software and network frameworks to interpret molecular structures' representations and establish relationships/correlations has enabled various research teams to develop numerous AI platforms for identifying new lead molecules or discovering new targets for already established drug molecules. The prediction of biological activity, ADME properties, and toxicity parameters in early stages have reduced the chances of failure and associated costs in later clinical stages, which was observed at a high rate in the tedious, expensive, and laborious drug discovery process. This review focuses on the different AI and machine learning (ML) techniques with their applications mainly focused on the pharmaceutical industry. The applications of AI frameworks in the identification of molecular target, hit identification/hit-to-lead optimization, analyzing drug-receptor interactions, drug repurposing, polypharmacology, synthetic accessibility, clinical trial design, and pharmaceutical developments are discussed in detail. We have also compiled the details of various startups in AI in this field. This review will provide a comprehensive analysis and outline various state-of-the-art AI/ML techniques to the readers with their framework applications. This review also highlights the challenges in this field, which need to be addressed for further success in pharmaceutical applications.

Autophagy3D: a comprehensive autophagy structure database

Autophagy pathway plays a central role in cellular degradation. The proteins involved in the core autophagy process are mostly localised on membranes or interact indirectly with lipid-associated proteins. Therefore, progress in structure determination of 'core autophagy proteins' remained relatively limited. Recent paradigm shift in structural biology that includes cutting-edge cryo-EM technology and robust AI-based Alphafold2 predicted models has significantly increased data points in biology. Here, we developed Autophagy3D, a web-based resource that provides an efficient way to access data associated with 40 core human autophagic proteins (80322 structures), their protein-protein interactors and ortholog structures from various species. Autophagy3D also offers detailed visualizations of protein structures, and, hence deriving direct biological insights. The database significantly enhances access to information as full datasets are available for download. The Autophagy3D can be publicly accessed via https://autophagy3d.igib.res.in. Database URL: https://autophagy3d.igib.res.in.

Interactions of Potential Endocrine-Disrupting Chemicals with Whole Human Proteome Predicted by AlphaFold2 Using an In Silico Approach

Binding with proteins is a critical molecular initiating event through which environmental pollutants exert toxic effects in humans. Previous studies have been limited by the availability of three-dimensional (3D) protein structures and have focused on only a small set of environmental contaminants. Using the highly accurate 3D protein structure predicted by AlphaFold2, this study explored over 60 million interactions obtained through molecular docking between 20,503 human proteins and 1251 potential endocrine-disrupting chemicals. A total of 66,613,773 docking results were obtained, 1.2% of which were considered to be high binding, as their docking scores were lower than -7. Monocyte to macrophage differentiation factor 2 (MMD2) was predicted to interact with the highest number of environmental pollutants (526), with polychlorinated biphenyls and polychlorinated dibenzofurans accounting for a significant proportion. Dimension reduction and clustering analysis revealed distinct protein profiles characterized by high binding affinities for perfluoroalkyl and polyfluoroalkyl substances (PFAS), phthalate-like chemicals, and other pollutants, consistent with their uniquely enriched pathways. Further structural analysis indicated that binding pockets with a high proportion of charged amino acid residues, relatively low α-helix content, and high β-sheet content were more likely to bind to PFAS than others. This study provides insights into the toxicity pathways of various pollutants impacting human health and offers novel perspectives for the establishment and expansion of adverse outcome pathway-based models.

Crystal structure and enzyme engineering of the broad substrate spectrum l-amino acid oxidase 4 from the fungus Hebeloma cylindrosporum

l-Amino acid oxidases (LAAOs) catalyze the oxidative deamination of l-amino acids to α-keto acids. Recombinant production of LAAOs with broad substrate spectrum remains a formidable challenge. We previously achieved this for the highly active and thermostable LAAO4 of Hebeloma cylindrosporum (HcLAAO4). Here, we crystallized a proteolytically truncated surface entropy reduction variant of HcLAAO4 and solved its structure in substrate-free form and in complex with diverse substrates. The ability to support the aliphatic portion of a substrate's side chain by an overall hydrophobic active site is responsible for the broad substrate spectrum of HcLAAO4, including l-amino acids with big aromatic, acidic and basic side chains. Based on the structural findings, we generated an E288H variant with increased activity toward pharmaceutical building blocks of high interest.

Structural basis of the Meinwald rearrangement catalysed by styrene oxide isomerase

Membrane-bound styrene oxide isomerase (SOI) catalyses the Meinwald rearrangement-a Lewis-acid-catalysed isomerization of an epoxide to a carbonyl compound-and has been used in single and cascade reactions. However, the structural information that explains its reaction mechanism has remained elusive. Here we determine cryo-electron microscopy (cryo-EM) structures of SOI bound to a single-domain antibody with and without the competitive inhibitor benzylamine, and elucidate the catalytic mechanism using electron paramagnetic resonance spectroscopy, functional assays, biophysical methods and docking experiments. We find ferric haem b bound at the subunit interface of the trimeric enzyme through H58, where Fe(III) acts as the Lewis acid by binding to the epoxide oxygen. Y103 and N64 and a hydrophobic pocket binding the oxygen of the epoxide and the aryl group, respectively, position substrates in a manner that explains the high regio-selectivity and stereo-specificity of SOI. Our findings can support extending the range of epoxide substrates and be used to potentially repurpose SOI for the catalysis of new-to-nature Fe-based chemical reactions.

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.

Computational approach for drug discovery against Gardnerella vaginalis in quest for safer and effective treatments for bacterial vaginosis

Bacterial vaginosis (BV), primarily attributed to Gardnerella vaginalis, poses significant challenges due to antibiotic resistance and suboptimal treatment outcomes. This study presents an integrated approach to identify potential drug targets and screen compounds against this bacterium by leveraging a computational methodology. Subtractive proteomics of the reference strain ASM286196v1/UMB0386 (assembly accession: GCA_002861965.1) facilitated the prioritization of proteins with essential bacterial functions and pathways as potential drug targets. We selected 3-deoxy-7-phosphoheptulonate synthase (aroG gene product; also known as DAHP synthase) for downstream analysis. Molecular docking was employed in PyRx (AutoDock Vina) to predict binding affinities between aroG inhibitors from the ZINC database and 3-deoxy-7-phosphoheptulonate synthase. Molecular dynamics simulations of 100 ns, using GROMACS, validated the stability of drug-target interactions. Additionally, ADMET profiling aided in the selection of compounds with favorable pharmacokinetic properties and safety profile for human hosts. PBPK profiling showed that ZINC98088375 had the highest bioavailability and efficient systemic circulation. Conversely, ZINC5113880 demonstrated the lowest absorption rate (39.661%). Moreover, cirrhosis, steatosis, and renal impairment appeared to influence blood concentration of the drug, impacting bioavailability. The integrative -omics approach utilized in this study underscores the potential of computer-aided drug design and offers a rational strategy for targeted inhibitor discovery against G. vaginalis. The strategy is an attempt to address the limitations of current BV treatments, including antibiotic resistance, and pave way for the development of safer and more effective therapeutics.

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.

Differential reliance of CTD-nuclear envelope phosphatase 1 on its regulatory subunit in ER lipid synthesis and storage

Lipin 1 is an ER enzyme that produces diacylglycerol, the lipid intermediate that feeds into the synthesis of glycerophospholipids for membrane expansion or triacylglycerol for storage into lipid droplets. CTD-Nuclear Envelope Phosphatase 1 (CTDNEP1) regulates lipin 1 to restrict ER membrane synthesis, but a role for CTDNEP1 in lipid storage in mammalian cells is not known. Furthermore, how NEP1R1, the regulatory subunit of CTDNEP1, contributes to these functions in mammalian cells is not fully understood. Here, we show that CTDNEP1 is reliant on NEP1R1 for its stability and function in limiting ER expansion. CTDNEP1 contains an amphipathic helix at its N-terminus that targets to the ER, nuclear envelope and lipid droplets. We identify key residues at the binding interface of CTDNEP1 and NEP1R1 and show that they facilitate complex formation in vivo and in vitro. We demonstrate that NEP1R1 binding to CTDNEP1 shields CTDNEP1 from proteasomal degradation to regulate lipin 1 and restrict ER size. Unexpectedly, NEP1R1 was not required for CTDNEP1's role in restricting lipid droplet biogenesis. Thus, the reliance of CTDNEP1 function on NEP1R1 depends on cellular demands for membrane production versus lipid storage. Together, our work provides a framework into understanding how the ER regulates lipid synthesis under different metabolic conditions.

Structural studies of intrinsically disordered MLL-fusion protein AF9 in complex with peptidomimetic inhibitors

AF9 (MLLT3) and its paralog ENL(MLLT1) are members of the YEATS family of proteins with important role in transcriptional and epigenetic regulatory complexes. These proteins are two common MLL fusion partners in MLL-rearranged leukemias. The oncofusion proteins MLL-AF9/ENL recruit multiple binding partners, including the histone methyltransferase DOT1L, leading to aberrant transcriptional activation and enhancing the expression of a characteristic set of genes that drive leukemogenesis. The interaction between AF9 and DOT1L is mediated by an intrinsically disordered C-terminal ANC1 homology domain (AHD) in AF9, which undergoes folding upon binding of DOT1L and other partner proteins. We have recently reported peptidomimetics that disrupt the recruitment of DOT1L by AF9 and ENL, providing a proof-of-concept for targeting AHD and assessing its druggability. Intrinsically disordered proteins, such as AF9 AHD, are difficult to study and characterize experimentally on a structural level. In this study, we present a successful protein engineering strategy to facilitate structural investigation of the intrinsically disordered AF9 AHD domain in complex with peptidomimetic inhibitors by using maltose binding protein (MBP) as a crystallization chaperone connected with linkers of varying flexibility and length. The strategic incorporation of disulfide bonds provided diffraction-quality crystals of the two disulfide-bridged MBP-AF9 AHD fusion proteins in complex with the peptidomimetics. These successfully determined first series of 2.1-2.6 Å crystal complex structures provide high-resolution insights into the interactions between AHD and its inhibitors, shedding light on the role of AHD in recruiting various binding partner proteins. We show that the overall complex structures closely resemble the reported NMR structure of AF9 AHD/DOT1L with notable difference in the conformation of the β-hairpin region, stabilized through conserved hydrogen bonds network. These first series of AF9 AHD/peptidomimetics complex structures are providing insights of the protein-inhibitor interactions and will facilitate further development of novel inhibitors targeting the AF9/ENL AHD domain.

Microenzymes: Is There Anybody Out There?

Biological macromolecules are found in different shapes and sizes. Among these, enzymes catalyze biochemical reactions and are essential in all organisms, but is there a limit size for them to function properly? Large enzymes such as catalases have hundreds of kDa and are formed by multiple subunits, whereas most enzymes are smaller, with molecular weights of 20-60 kDa. Enzymes smaller than 10 kDa could be called microenzymes and the present literature review brings together evidence of their occurrence in nature. Additionally, bioactive peptides could be a natural source for novel microenzymes hidden in larger peptides and molecular downsizing could be useful to engineer artificial enzymes with low molecular weight improving their stability and heterologous expression. An integrative approach is crucial to discover and determine the amino acid sequences of novel microenzymes, together with their genomic identification and their biochemical biological and evolutionary functions.

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

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

AlphaFold2 modeling and molecular dynamics simulations of an intrinsically disordered protein

We use AlphaFold2 (AF2) to model the monomer and dimer structures of an intrinsically disordered protein (IDP), Nvjp-1, assisted by molecular dynamics (MD) simulations. We observe relatively rigid dimeric structures of Nvjp-1 when compared with the monomer structures. We suggest that protein conformations from multiple AF2 models and those from MD trajectories exhibit a coherent trend: the conformations of an IDP are deviated from each other and the conformations of a well-folded protein are consistent with each other. We use a residue-residue interaction network (RIN) derived from the contact map which show that the residue-residue interactions in Nvjp-1 are mainly transient; however, those in a well-folded protein are mainly persistent. Despite the variation in 3D shapes, we show that the AF2 models of both disordered and ordered proteins exhibit highly consistent profiles of the pLDDT (predicted local distance difference test) scores. These results indicate a potential protocol to justify the IDPs based on multiple AF2 models and MD simulations.

Ligand Gaussian accelerated Molecular Dynamics 3 (LiGaMD3): Improved Calculations of Binding Thermodynamics and Kinetics of Both Small Molecules and Flexible Peptides

Binding thermodynamics and kinetics play critical roles in drug design. However, it has proven challenging to efficiently predict ligand binding thermodynamics and kinetics of small molecules and flexible peptides using conventional Molecular Dynamics (cMD), due to limited simulation timescales. Based on our previously developed Ligand Gaussian accelerated Molecular Dynamics (LiGaMD) method, we present a new approach, termed "LiGaMD3", in which we introduce triple boosts into three individual energy terms that play important roles in small-molecule/peptide dissociation, rebinding and system conformational changes to improve the sampling efficiency of small-molecule/peptide interactions with target proteins. To validate the performance of LiGaMD3, MDM2 bound by a small molecule (Nutlin 3) and two highly flexible peptides (PMI and P53) were chosen as model systems. LiGaMD3 could efficiently capture repetitive small-molecule/peptide dissociation and binding events within 2 microsecond simulations. The predicted binding kinetic constant rates and free energies from LiGaMD3 agreed with available experimental values and previous simulation results. Therefore, LiGaMD3 provides a more general and efficient approach to capture dissociation and binding of both small-molecule ligand and flexible peptides, allowing for accurate prediction of their binding thermodynamics and kinetics.

An AlphaFold Structure Analysis of COQ2 as Key a Component of the Coenzyme Q Synthesis Complex

Coenzyme Q (CoQ) is a lipidic compound that is widely distributed in nature, with crucial functions in metabolism, protection against oxidative damage and ferroptosis and other processes. CoQ biosynthesis is a conserved and complex pathway involving several proteins. COQ2 is a member of the UbiA family of transmembrane prenyltransferases that catalyzes the condensation of the head and tail precursors of CoQ, which is a key step in the process, because its product is the first intermediate that will be modified in the head by the next components of the synthesis process. Mutations in this protein have been linked to primary CoQ deficiency in humans, a rare disease predominantly affecting organs with a high energy demand. The reaction catalyzed by COQ2 and its mechanism are still unknown. Here, we aimed at clarifying the COQ2 reaction by exploring possible substrate binding sites using a strategy based on homology, comprising the identification of available ligand-bound homologs with solved structures in the Protein Data Bank (PDB) and their subsequent structural superposition in the AlphaFold predicted model for COQ2. The results highlight some residues located on the central cavity or the matrix loops that may be involved in substrate interaction, some of which are mutated in primary CoQ deficiency patients. Furthermore, we analyze the structural modifications introduced by the pathogenic mutations found in humans. These findings shed new light on the understanding of COQ2's function and, thus, CoQ's biosynthesis and the pathogenicity of primary CoQ deficiency.

Inference of drug off-target effects on cellular signaling using interactome-based deep learning

Many diseases emerge from dysregulated cellular signaling, and drugs are often designed to target specific signaling proteins. Off-target effects are, however, common and may ultimately result in failed clinical trials. Here we develop a computer model of the cell's transcriptional response to drugs for improved understanding of their mechanisms of action. The model is based on ensembles of artificial neural networks and simultaneously infers drug-target interactions and their downstream effects on intracellular signaling. With this, it predicts transcription factors' activities, while recovering known drug-target interactions and inferring many new ones, which we validate with an independent dataset. As a case study, we analyze the effects of the drug Lestaurtinib on downstream signaling. Alongside its intended target, FLT3, the model predicts an inhibition of CDK2 that enhances the downregulation of the cell cycle-critical transcription factor FOXM1. Our approach can therefore enhance our understanding of drug signaling for therapeutic design.

BioNexusSentinel: a visual tool for bioregulatory network and cytohistological RNA-seq genetic expression profiling within the context of multicellular simulation research using ChatGPT-augmented software engineering

Summary

Motivated by the need to parameterize ongoing multicellular simulation research, this paper documents the culmination of a ChatGPT augmented software engineering cycle resulting in an integrated visual platform for efficient cytohistological RNA-seq and bioregulatory network exploration. As contrasted to other systems and synthetic biology tools, BioNexusSentinel was developed de novo to uniquely combine these features. Reactome served as the primary source of remotely accessible biological models, accessible using BioNexusSentinel's novel search engine and REST API requests. The innovative, feature-rich gene expression profiler component was developed to enhance the exploratory experience for the researcher, culminating in the cytohistological RNA-seq explorer based on Human Protein Atlas data. A novel cytohistological classifier would be integrated via pre-processed analysis of the RNA-seq data via R statistical language, providing for useful analytical functionality and good performance for the end-user. Implications of the work span prospects for model orthogonality evaluations, gap identification in network modelling, prototyped automatic kinetics parameterization, and downstream simulation and cellular biological state analysis. This unique computational biology software engineering collaboration with generative natural language processing artificial intelligence was shown to enhance worker productivity, with evident benefits in terms of accelerating coding and machine-human intelligence transfer.

Availability and implementation

BioNexusSentinel project releases, with corresponding data and installation instructions, are available at https://github.com/RichardMatzko/BioNexusSentinel.

Modeling Flexible Protein Structure With AlphaFold2 and Crosslinking Mass Spectrometry

We propose a pipeline that combines AlphaFold2 (AF2) and crosslinking mass spectrometry (XL-MS) to model the structure of proteins with multiple conformations. The pipeline consists of two main steps: ensemble generation using AF2 and conformer selection using XL-MS data. For conformer selection, we developed two scores-the monolink probability score (MP) and the crosslink probability score (XLP)-both of which are based on residue depth from the protein surface. We benchmarked MP and XLP on a large dataset of decoy protein structures and showed that our scores outperform previously developed scores. We then tested our methodology on three proteins having an open and closed conformation in the Protein Data Bank: Complement component 3 (C3), luciferase, and glutamine-binding periplasmic protein, first generating ensembles using AF2, which were then screened for the open and closed conformations using experimental XL-MS data. In five out of six cases, the most accurate model within the AF2 ensembles-or a conformation within 1 Å of this model-was identified using crosslinks, as assessed through the XLP score. In the remaining case, only the monolinks (assessed through the MP score) successfully identified the open conformation of glutamine-binding periplasmic protein, and these results were further improved by including the "occupancy" of the monolinks. This serves as a compelling proof-of-concept for the effectiveness of monolinks. In contrast, the AF2 assessment score was only able to identify the most accurate conformation in two out of six cases. Our results highlight the complementarity of AF2 with experimental methods like XL-MS, with the MP and XLP scores providing reliable metrics to assess the quality of the predicted models. The MP and XLP scoring functions mentioned above are available at https://gitlab.com/topf-lab/xlms-tools.

The whack-a-mole governance challenge for AI-enabled synthetic biology: literature review and emerging frameworks

AI-enabled synthetic biology has tremendous potential but also significantly increases biorisks and brings about a new set of dual use concerns. The picture is complicated given the vast innovations envisioned to emerge by combining emerging technologies, as AI-enabled synthetic biology potentially scales up bioengineering into industrial biomanufacturing. However, the literature review indicates that goals such as maintaining a reasonable scope for innovation, or more ambitiously to foster a huge bioeconomy do not necessarily contrast with biosafety, but need to go hand in hand. This paper presents a literature review of the issues and describes emerging frameworks for policy and practice that transverse the options of command-and-control, stewardship, bottom-up, and laissez-faire governance. How to achieve early warning systems that enable prevention and mitigation of future AI-enabled biohazards from the lab, from deliberate misuse, or from the public realm, will constantly need to evolve, and adaptive, interactive approaches should emerge. Although biorisk is subject to an established governance regime, and scientists generally adhere to biosafety protocols, even experimental, but legitimate use by scientists could lead to unexpected developments. Recent advances in chatbots enabled by generative AI have revived fears that advanced biological insight can more easily get into the hands of malignant individuals or organizations. Given these sets of issues, society needs to rethink how AI-enabled synthetic biology should be governed. The suggested way to visualize the challenge at hand is whack-a-mole governance, although the emerging solutions are perhaps not so different either.

AlphaFold modeling of nepovirus 3C-like proteinases provides new insights into their diverse substrate specificities

The majority of picornaviral 3C proteinases (3Cpro) cleavage sites possess glutamine at the P1 position. Plant nepovirus 3C-like proteinases (3CLpro) show however much broader specificity, cleaving not only after glutamine, but also after several basic and hydrophobic residues. To investigate this difference, we employed AlphaFold to generate structural models of twelve selected 3CLpro, representing six substrate specificities. Generally, we observed favorable correlations between the architecture and charge of nepovirus proteinase S1 subsites and their ability to accept or restrict larger residues. The models identified a conserved aspartate residue close to the P1 residue in the S1 subsites of all nepovirus proteinases examined, consistent with the observed strong bias against negatively-charged residues at the P1 position of nepovirus cleavage sites. Finally, a cramped S4 subsite along with the presence of two unique histidine and serine residues explains the strict requirement of the grapevine fanleaf virus proteinase for serine at the P4 position.

Cryo-EM structure of a 16.5-kDa small heat-shock protein from Methanocaldococcus jannaschii

The small heat-shock protein (sHSP) from the archaea Methanocaldococcus jannaschii, MjsHSP16.5, functions as a broad substrate ATP-independent holding chaperone protecting misfolded proteins from aggregation under stress conditions. This protein is the first sHSP characterized by X-ray crystallography, thereby contributing significantly to our understanding of sHSPs. However, despite numerous studies assessing its functions and structures, the precise arrangement of the N-terminal domains (NTDs) within this sHSP cage remains elusive. Here we present the cryo-electron microscopy (cryo-EM) structure of MjsHSP16.5 at 2.49-Å resolution. The subunits of MjsHSP16.5 in the cryo-EM structure exhibit lesser compaction compared to their counterparts in the crystal structure. This structural feature holds particular significance in relation to the biophysical properties of MjsHSP16.5, suggesting a close resemblance to this sHSP native state. Additionally, our cryo-EM structure unveils the density of residues 24-33 within the NTD of MjsHSP16.5, a feature that typically remains invisible in the majority of its crystal structures. Notably, these residues show a propensity to adopt a β-strand conformation and engage in antiparallel interactions with strand β1, both intra- and inter-subunit modes. These structural insights are corroborated by structural predictions, disulfide bond cross-linking studies of Cys-substitution mutants, and protein disaggregation assays. A comprehensive understanding of the structural features of MjsHSP16.5 expectedly holds the potential to inspire a wide range of interdisciplinary applications, owing to the renowned versatility of this sHSP as a nanoscale protein platform.

WIV, a protein domain found in a wide number of arthropod viruses, which probably facilitates infection

The most powerful approach to detect distant homologues of a protein is based on structure prediction and comparison. Yet this approach is still inapplicable to many viral proteins. Therefore, we applied a powerful sequence-based procedure to identify distant homologues of viral proteins. It relies on three principles: (1) traces of sequence similarity can persist beyond the significance cutoff of homology detection programmes; (2) candidate homologues can be identified among proteins with weak sequence similarity to the query by using 'contextual' information, e.g. taxonomy or type of host infected; (3) these candidate homologues can be validated using highly sensitive profile-profile comparison. As a test case, this approach was applied to a protein without known homologues, encoded by ORF4 of Lake Sinai viruses (which infect bees). We discovered that the ORF4 protein contains a domain that has homologues in proteins from >20 taxa of viruses infecting arthropods. We called this domain 'widespread, intriguing, versatile' (WIV), because it is found in proteins with a wide variety of functions and within varied domain contexts. For example, WIV is found in the NSs protein of tospoviruses, a global threat to food security, which infect plants as well as their arthropod vectors; in the RNA2 ORF1-encoded protein of chronic bee paralysis virus, a widespread virus of bees; and in various proteins of cypoviruses, which infect the silkworm Bombyx mori. Structural modelling with AlphaFold indicated that the WIV domain has a previously unknown fold, and bibliographical evidence suggests that it facilitates infection of arthropods.

Ligand- and Structure-based Approaches for Transmembrane Transporter Modeling

The study of transporter proteins is key to understanding the mechanism behind multidrug resistance and drug-drug interactions causing severe side effects. While ATP-binding transporters are well-studied, solute carriers illustrate an understudied family with a high number of orphan proteins. To study these transporters, in silico methods can be used to shed light on the basic molecular machinery by studying protein-ligand interactions. Nowadays, computational methods are an integral part of the drug discovery and development process. In this short review, computational approaches, such as machine learning, are discussed, which try to tackle interactions between transport proteins and certain compounds to locate target proteins. Furthermore, a few cases of selected members of the ATP binding transporter and solute carrier family are covered, which are of high interest in clinical drug interaction studies, especially for regulatory agencies. The strengths and limitations of ligand-based and structure-based methods are discussed to highlight their applicability for different studies. Furthermore, the combination of multiple approaches can improve the information obtained to find crucial amino acids that explain important interactions of protein-ligand complexes in more detail. This allows the design of drug candidates with increased activity towards a target protein, which further helps to support future synthetic efforts.

Deep learning applications in protein crystallography

Deep learning techniques can recognize complex patterns in noisy, multidimensional data. In recent years, researchers have started to explore the potential of deep learning in the field of structural biology, including protein crystallography. This field has some significant challenges, in particular producing high-quality and well ordered protein crystals. Additionally, collecting diffraction data with high completeness and quality, and determining and refining protein structures can be problematic. Protein crystallographic data are often high-dimensional, noisy and incomplete. Deep learning algorithms can extract relevant features from these data and learn to recognize patterns, which can improve the success rate of crystallization and the quality of crystal structures. This paper reviews progress in this field.

Advances in biosynthesis of peptide drugs: Technology and industrialization

Peptide drugs are developed from endogenous or synthetic peptides with specific biological activities. They have advantages of strong target specificity, high efficacy and low toxicity, thus showing great promise in the treatment of many diseases such as cancer, infections, and diabetes. Although an increasing number of peptide drugs have entered market in recent years, the preparation of peptide drug substances is yet a bottleneck problem for their industrial production. Comparing to the chemical synthesis method, peptide biosynthesis has advantages of simple synthesis, low cost, and low contamination. Therefore, the biosynthesis technology of peptide drugs has been widely used for manufacturing. Herein, we reviewed the development of peptide drugs and recent advances in peptide biosynthesis technology, in order to shed a light to the prospect of industrial production of peptide drugs based on biosynthesis technology.

ChatGPT in research and health professions education: challenges, opportunities, and future directions

ChatGPT was launched by OpenAI in November 2022 and within 2 months it became popular across a wide range of industrial, social, and intellectual contexts including healthcare education. This article reviews the impact of ChatGPT on research and health professions education by identifying the challenges and opportunities in these fields. Additionally, it aims to provide future directions to mitigate the challenges and maximize the benefits of this technology in health professions education. ChatGPT has the potential to revolutionize the field of research and health professions education. However, there is a need to address ethical concerns and limitations such as lack of real-time data, data inaccuracies, biases, plagiarism, and copyright infringement before its implementation. Future research can highlight the ways to mitigate these challenges; establish guidelines and policies; and explore how effectively ChatGPT and other AI tools can be used in the field of research and healthcare professions education.

A multi-reservoir extruder for time-resolved serial protein crystallography and compound screening at X-ray free-electron lasers

Serial crystallography at X-ray free-electron lasers (XFELs) permits the determination of radiation-damage free static as well as time-resolved protein structures at room temperature. Efficient sample delivery is a key factor for such experiments. Here, we describe a multi-reservoir, high viscosity extruder as a step towards automation of sample delivery at XFELs. Compared to a standard single extruder, sample exchange time was halved and the workload of users was greatly reduced. In-built temperature control of samples facilitated optimal extrusion and supported sample stability. After commissioning the device with lysozyme crystals, we collected time-resolved data using crystals of a membrane-bound, light-driven sodium pump. Static data were also collected from the soluble protein tubulin that was soaked with a series of small molecule drugs. Using these data, we identify low occupancy (as little as 30%) ligands using a minimal amount of data from a serial crystallography experiment, a result that could be exploited for structure-based drug design.

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

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

Differential reliance of CTD-nuclear envelope phosphatase 1 on its regulatory subunit in ER lipid synthesis and storage

The endoplasmic reticulum (ER) is the site for the synthesis of the major membrane and storage lipids. Lipin 1 produces diacylglycerol, the lipid intermediate critical for the synthesis of both membrane and storage lipids in the ER. CTD-Nuclear Envelope Phosphatase 1 (CTDNEP1) regulates lipin 1 to restrict ER membrane synthesis, but its role in lipid storage in mammalian cells is unknown. Here, we show that the ubiquitin-proteasome degradation pathway controls the levels of ER/nuclear envelope-associated CTDNEP1 to regulate ER membrane synthesis through lipin 1. The N-terminus of CTDNEP1 is an amphipathic helix that targets to the ER, nuclear envelope and lipid droplets. We identify key residues at the binding interface of CTDNEP1 with its regulatory subunit NEP1R1 and show that they facilitate complex formation in vivo and in vitro . We demonstrate a role for NEP1R1 in temporarily shielding CTDNEP1 from proteasomal degradation to regulate lipin 1 and restrict ER size. Unexpectedly, we found that NEP1R1 is not required for CTDNEP1's role in restricting lipid droplet biogenesis. Thus, the reliance of CTDNEP1 function on its regulatory subunit differs during ER membrane synthesis and lipid storage. Together, our work provides a framework into understanding how the ER regulates lipid synthesis and storage under fluctuating conditions.

Dissecting the Molecular Basis of Host Leucine-Rich Repeat Containing 15 Mediated Interaction with Receptor Binding Domain of SARS-CoV-2 Spike Protein: A Computational Approach

The detection of leucine-rich repeat containing 15 (LRRC15) as a connecting link with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) underscores the possibility of its involvement in differential restriction activity of SARS-CoV-2 pathways. However, the structure-function mechanism of LRRC15 involving the receptor binding domain (RBD) of the SARS-CoV-2 spike protein and their mode of interaction is largely unknown. Using state-of-the-art AlphaFold2 and all-atom molecular dynamics simulations, our findings provide evidences of alternative binding modes of RBD with LRR units of LRRC15 having varied affinities. Contribution of both the receptor binding regions in RBD, including receptor binding motif in accommodating the LRR domain, towards the C-terminal region, emphasizes its differential role in modulating host cell receptiveness for SARS-CoV-2, the innate immune system, as well as antiviral tone. However, further experimental validations are necessary for unravelling the unknown mechanism and distinctive features of this host receptor in the COVID-19 pandemic, involving both the transmembrane as well as cytoplasmic domain.

Computational analysis of regulatory regions in human protein kinases

Eukaryotic proteins often feature modular domain structures comprising globular domains that are connected by linker regions and intrinsically disordered regions that may contain important functional motifs. The intramolecular interactions of globular domains and nonglobular regions can play critical roles in different aspects of protein function. However, studying these interactions and their regulatory roles can be challenging due to the flexibility of nonglobular regions, the long insertions separating interacting modules, and the transient nature of some interactions. Obtaining the experimental structures of multiple domains and functional regions is more difficult than determining the structures of individual globular domains. High-quality structural models generated by AlphaFold offer a unique opportunity to study intramolecular interactions in eukaryotic proteins. In this study, we systematically explored intramolecular interactions between human protein kinase domains (KDs) and potential regulatory regions, including globular domains, N- and C-terminal tails, long insertions, and distal nonglobular regions. Our analysis identified intramolecular interactions between human KDs and 35 different types of globular domains, exhibiting a variety of interaction modes that could contribute to orthosteric or allosteric regulation of kinase activity. We also identified prevalent interactions between human KDs and their flanking regions (N- and C-terminal tails). These interactions exhibit group-specific characteristics and can vary within each specific kinase group. Although long-range interactions between KDs and nonglobular regions are relatively rare, structural details of these interactions offer new insights into the regulation mechanisms of several kinases, such as HASPIN, MAPK7, MAPK15, and SIK1B.

Seeking the interspecies crosswalk for filamentous microbe effectors

Both pathogenic and symbiotic microorganisms modulate the immune response and physiology of their host to establish a suitable niche. Key players in mediating colonization outcome are microbial effector proteins that act either inside (cytoplasmic) or outside (apoplastic) the plant cells and modify the abundance or activity of host macromolecules. We compile novel insights into the much-disputed processes of effector secretion and translocation of filamentous organisms, namely fungi and oomycetes. We report how recent studies that focus on unconventional secretion and effector structure challenge the long-standing image of effectors as conventionally secreted proteins that are translocated with the aid of primary amino acid sequence motifs. Furthermore, we emphasize the potential of diverse, unbiased, state-of-the-art proteomics approaches in the holistic characterization of fungal and oomycete effectomes.