The impact of AlphaFold Protein Structure Database on the fields of life sciences

The latest progress in assay development in leishmaniasis drug discovery: a review of the available papers on PubMed from the past year

INTRODUCTION

Leishmaniasis is a significant neglected tropical disease with limited treatment options that urgently requires ongoing efforts in drug discovery. Recent advances have focused on the development of new assays and methods to identify effective therapeutic candidates.

AREAS COVERED

This review explores recent trends and methodologies in leishmaniasis drug discovery, with a particular focus on in silico and in vitro studies, as well as in vivo validation, using animal models. A detailed analysis of recent studies was provided, discussing the methodologies employed, such as manual and automated parasite quantification, and the use of fluorescence and luminescence-based techniques. Additionally, global research trends were analyzed, highlighting the leading countries in scientific output and the collaborative efforts driving advancements in this field.

EXPERT OPINION

The field of leishmaniasis drug discovery has rapidly progressed in the last years, but the lack of standardized methodologies and limited in vivo validation remain significant hurdles. To advance promising treatments to clinical trials, cross-validation of preclinical findings and interdisciplinary collaboration are essential. Increased funding and global partnerships are also crucial to accelerate the discovery and development of alternative and effective therapies.

Revisiting Aspergillus terreus MTCC6324 recombinant alcohol oxidase (rAOx): enhanced in-silico insight into structure, function, and substrate sequestration mechanism

Alcohol oxidase (AOx) enzymes have gained significant attention for their potential in industrial applications due to their unique ability to catalyze irreversible oxidation of diverse alcohol substrates without external co-factors. This study revisits and enhances in-silico work on Aspergillus terreus MTCC6324 recombinant AOx (rAOx) enzyme, combining artificial intelligence (AlphaFold), molecular docking (AutoDock Vina) techniques and Molecular dynamics (MD) simulations (Desmond). Comprehensive sequence analysis revealed a high degree of conservation among 23 AOx amino acid sequences from various Aspergillus species highlights conserved regions, affirming its GXGXXG Rossmann fold motif. AlphaFold-predicted 3D structure of rAOx demonstrated improved stereo-chemical stability compared to I-TASSER predicted structure with 87.6% amino acid residues in most favourable region of Ramachandran plot compared to 79.5%, respectively. Molecular docking revealed the binding affinities of co-factors FAD and diverse alcohol substrates, with cinnamyl alcohol exhibiting robust interaction with rAOx holoenzyme. MD simulations further elucidate the stability and dynamics of rAOx-FAD-cinnamyl alcohol complex over 100 nanoseconds. The simulations showcase FAD's stable binding within the protein core and highlights transient substrate interactions, dissociating within the active site after 75 ns suggesting a substrate sequestration mechanism. The study unveils substrate sequestration mechanism wherein cinnamyl alcohol exhibits temporary binding, leading to quick detachment from active site, mimicking reported exponential kinetics. This study not only validates previous findings but also offers a comprehensive understanding of intricate dynamics governing rAOx enzymatic activity. The improved sequence-to-structure prediction and detailed molecular insights into substrate sequestration provide a valuable foundation for future experimental investigations and rational design of bio-catalytic processes.

AlphaFold Meets De Novo Drug Design: Leveraging Structural Protein Information in Multitarget Molecular Generative Models

Recent advancements in deep learning and generative models have significantly expanded the applications of virtual screening for drug-like compounds. Here, we introduce a multitarget transformer model, PCMol, that leverages the latent protein embeddings derived from AlphaFold2 as a means of conditioning a de novo generative model on different targets. Incorporating rich protein representations allows the model to capture their structural relationships, enabling the chemical space interpolation of active compounds and target-side generalization to new proteins based on embedding similarities. In this work, we benchmark against other existing target-conditioned transformer models to illustrate the validity of using AlphaFold protein representations over raw amino acid sequences. We show that low-dimensional projections of these protein embeddings cluster appropriately based on target families and that model performance declines when these representations are intentionally corrupted. We also show that the PCMol model generates diverse, potentially active molecules for a wide array of proteins, including those with sparse ligand bioactivity data. The generated compounds display higher similarity known active ligands of held-out targets and have comparable molecular docking scores while maintaining novelty. Additionally, we demonstrate the important role of data augmentation in bolstering the performance of generative models in low-data regimes. Software package and AlphaFold protein embeddings are freely available at https://github.com/CDDLeiden/PCMol.

Mutations that prevent phosphorylation of the BMP4 prodomain impair proteolytic maturation of homodimers leading to early lethality in mice

Bone morphogenetic protein4 (BMP4) plays numerous roles during embryogenesis and can signal either as a homodimer, or as a more active BMP4/7 heterodimer. BMPs are generated as inactive precursor proteins that dimerize and are cleaved to generate the bioactive ligand and inactive prodomain fragments. In humans, heterozygous mutations within the prodomain of BMP4 are associated with birth defects. We studied the effect of two of these mutations (p.S91C and p.E93G), which disrupt a conserved FAM20C phosphorylation motif, on ligand activity. We compared the activity of BMP4 homodimers or heterodimers generated from BMP4, BMP4S91C or BMP4E93G precursor proteins in Xenopus embryos and found that these mutations reduce the activity of BMP4 homodimers but not heterodimers. We generated Bmp4 S91C and Bmp4 E93G knock-in mice and found that Bmp4 S91C/S91C mice die by E11.5 and display reduced BMP activity in multiple tissues including the heart at E10.5. Most Bmp4 E93G/E93G mice die before weaning and Bmp4 -/E93G mutants die prenatally with reduced or absent eyes, heart and ventral body wall closure defects. Mouse embryonic fibroblasts (MEFs) isolated from Bmp4 S91C and Bmp4 E93G embryos show accumulation of BMP4 precursor protein, reduced levels of cleaved BMP ligand and reduced BMP activity relative to MEFs from wild type littermates. Because Bmp7 is not expressed in MEFs, the accumulation of unprocessed BMP4 precursor protein in mice carrying these mutations most likely reflects an inability to cleave BMP4 homodimers, leading to reduced levels of cleaved ligand and BMP activity in vivo. Our results suggest that phosphorylation of the BMP4 prodomain is required for proteolytic activation of BMP4 homodimers, but not heterodimers.

Harnessing AlphaFold to reveal hERG channel conformational state secrets

To design safe, selective, and effective new therapies, there must be a deep understanding of the structure and function of the drug target. One of the most difficult problems to solve has been resolution of discrete conformational states of transmembrane ion channel proteins. An example is KV11.1 (hERG), comprising the primary cardiac repolarizing current, I Kr. hERG is a notorious drug anti-target against which all promising drugs are screened to determine potential for arrhythmia. Drug interactions with the hERG inactivated state are linked to elevated arrhythmia risk, and drugs may become trapped during channel closure. However, the structural details of multiple conformational states have remained elusive. Here, we guided AlphaFold2 to predict plausible hERG inactivated and closed conformations, obtaining results consistent with multiple available experimental data. Drug docking simulations demonstrated hERG state-specific drug interactions in good agreement with experimental results, revealing that most drugs bind more effectively in the inactivated state and are trapped in the closed state. Molecular dynamics simulations demonstrated ion conduction for an open but not AlphaFold2 predicted inactivated state that aligned with earlier studies. Finally, we identified key molecular determinants of state transitions by analyzing interaction networks across closed, open, and inactivated states in agreement with earlier mutagenesis studies. Here, we demonstrate a readily generalizable application of AlphaFold2 as an effective and robust method to predict discrete protein conformations, reconcile seemingly disparate data and identify novel linkages from structure to function.

The dual role of a novel Sinorhizobium meliloti chemotaxis protein CheT in signal termination and adaptation

Sinorhizobium meliloti senses nutrients and compounds exuded from alfalfa host roots and coordinates an excitation, termination, and adaptation pathway during chemotaxis. We investigated the role of the novel S. meliloti chemotaxis protein CheT. While CheT and the Escherichia coli phosphatase CheZ share little sequence homology, CheT is predicted to possess an α-helix with a DXXXQ phosphatase motif. Phosphorylation assays demonstrated that CheT dephosphorylates the phosphate-sink response regulator, CheY1~P by enhancing its decay two-fold but does not affect the motor response regulator CheY2~P. Isothermal Titration Calorimetry (ITC) experiments revealed that CheT binds to a phosphomimic of CheY1~P with a KD of 2.9 μM, which is 25-fold stronger than its binding to CheY1. Dissimilar chemotaxis phenotypes of the ΔcheT mutant and cheT DXXXQ phosphatase mutants led to the hypothesis that CheT exerts additional function(s). A screen for potential binding partners of CheT revealed that it forms a complex with the methyltransferase CheR. ITC experiments confirmed CheT/CheR binding with a KD of 19 μM, and a SEC-MALS analysis determined a 1:1 and 2:1 CheT/CheR complex formation. Although they did not affect each other's enzymatic activity, CheT binding to CheY1~P and CheR may serve as a link between signal termination and sensory adaptation.

An outlook on structural biology after AlphaFold: tools, limits and perspectives

AlphaFold and similar groundbreaking, AI-based tools, have revolutionized the field of structural bioinformatics, with their remarkable accuracy in ab-initio protein structure prediction. This success has catalyzed the development of new software and pipelines aimed at incorporating AlphaFold's predictions, often focusing on addressing the algorithm's remaining challenges. Here, we present the current landscape of structural bioinformatics shaped by AlphaFold, and discuss how the field is dynamically responding to this revolution, with new software, methods, and pipelines. While the excitement around AI-based tools led to their widespread application, it is essential to acknowledge that their practical success hinges on their integration into established protocols within structural bioinformatics, often neglected in the context of AI-driven advancements. Indeed, user-driven intervention is still as pivotal in the structure prediction process as in complementing state-of-the-art algorithms with functional and biological knowledge.

Reliability of AlphaFold2 Models in Virtual Drug Screening: A Focus on Selected Class A GPCRs

Protein three-dimensional (3D) structure prediction is one of the most challenging issues in the field of computational biochemistry, which has overwhelmed scientists for almost half a century. A significant breakthrough in structural biology has been established by developing the artificial intelligence (AI) system AlphaFold2 (AF2). The AF2 system provides a state-of-the-art prediction of protein structures from nearly all known protein sequences with high accuracy. This study examined the reliability of AF2 models compared to the experimental structures in drug discovery, focusing on one of the most common protein drug-targeted classes known as G protein-coupled receptors (GPCRs) class A. A total of 32 representative protein targets were selected, including experimental structures of X-ray crystallographic and Cryo-EM structures and their corresponding AF2 models. The quality of AF2 models was assessed using different structure validation tools, including the pLDDT score, RMSD value, MolProbity score, percentage of Ramachandran favored, QMEAN Z-score, and QMEANDisCo Global. The molecular docking was performed using the Genetic Optimization for Ligand Docking (GOLD) software. The AF2 models' reliability in virtual drug screening was determined by their ability to predict the ligand binding poses closest to the native binding pose by assessing the Root Mean Square Deviation (RMSD) metric and docking scoring function. The quality of the docking and scoring function was evaluated using the enrichment factor (EF). Furthermore, the capability of using AF2 models in molecular docking to identify hits with key protein-ligand interactions was analyzed. The posing power results showed that the AF2 models successfully predicted ligand binding poses (RMSD < 2 Å). However, they exhibited lower screening power, with average EF values of 2.24, 2.42, and 1.82 for X-ray, Cryo-EM, and AF2 structures, respectively. Moreover, our study revealed that molecular docking using AF2 models can identify competitive inhibitors. In conclusion, this study found that AF2 models provided docking results comparable to experimental structures, particularly for certain GPCR targets, and could potentially significantly impact drug discovery.

Investigation of protein family relationships with deep learning

Motivation

In this article, we propose a method for finding similarities between Pfam families based on the pre-trained neural network ProtENN2. We use the model ProtENN2 per-residue embeddings to produce new high-dimensional per-family embeddings and develop an approach for calculating inter-family similarity scores based on these embeddings, and evaluate its predictions using structure comparison.

Results

We apply our method to Pfam annotation by refining clan membership for Pfam families, suggesting both new members of existing clans and potential new clans for future Pfam releases. We investigate some of the failure modes of our approach, which suggests directions for future improvements. Our method is relatively simple with few parameters and could be applied to other protein family classification models. Overall, our work suggests potential benefits of employing deep learning for improving our understanding of protein family relationships and functions of previously uncharacterized families.

Availability and implementation

github.com/iponamareva/ProtCNNSim, 10.5281/zenodo.10091909.

Generative artificial intelligence performs rudimentary structural biology modeling

Natural language-based generative artificial intelligence (AI) has become increasingly prevalent in scientific research. Intriguingly, capabilities of generative pre-trained transformer (GPT) language models beyond the scope of natural language tasks have recently been identified. Here we explored how GPT-4 might be able to perform rudimentary structural biology modeling. We prompted GPT-4 to model 3D structures for the 20 standard amino acids and an α-helical polypeptide chain, with the latter incorporating Wolfram mathematical computation. We also used GPT-4 to perform structural interaction analysis between the anti-viral nirmatrelvir and its target, the SARS-CoV-2 main protease. Geometric parameters of the generated structures typically approximated close to experimental references. However, modeling was sporadically error-prone and molecular complexity was not well tolerated. Interaction analysis further revealed the ability of GPT-4 to identify specific amino acid residues involved in ligand binding along with corresponding bond distances. Despite current limitations, we show the current capacity of natural language generative AI to perform basic structural biology modeling and interaction analysis with atomic-scale accuracy.

A gene-rich mitochondrion with a unique ancestral protein transport system

Mitochondria originated from an ancient endosymbiosis involving an alphaproteobacterium.1,2,3 Over time, these organelles reduced their gene content massively, with most genes being transferred to the host nucleus before the last eukaryotic common ancestor (LECA).4 This process has yielded varying gene compositions in modern mitogenomes, including the complete loss of this organellar genome in some extreme cases.5,6,7,8,9,10,11,12,13,14 At the other end of the spectrum, jakobids harbor the most gene-rich mitogenomes, encoding 60-66 proteins.8 Here, we introduce the mitogenome of Mantamonas sphyraenae, a protist from the deep-branching CRuMs supergroup.15,16 Remarkably, it boasts the most gene-rich mitogenome outside of jakobids, by housing 91 genes, including 62 protein-coding ones. These include rare homologs of the four subunits of the bacterial-type cytochrome c maturation system I (CcmA, CcmB, CcmC, and CcmF) alongside a unique ribosomal protein S6. During the early evolution of mitochondria, gene transfer from the proto-mitochondrial endosymbiont to the nucleus became possible thanks to systems facilitating the transport of proteins synthesized in the host cytoplasm back to the mitochondrion. In addition to the universally found eukaryotic protein import systems, jakobid mitogenomes were reported to uniquely encode the SecY transmembrane protein of the Sec general secretory pathway, whose evolutionary origin was however unclear. The Mantamonas mitogenome not only encodes SecY but also SecA, SecE, and SecG, making it the sole eukaryote known to house a complete mitochondrial Sec translocation system. Furthermore, our phylogenetic and comparative genomic analyses provide compelling evidence for the alphaproteobacterial origin of this system, establishing its presence in LECA.

Complete genome sequences of two closely related isolates of Staphylococcus saprophyticus isolated from human fingertips

Complete genomes of two closely related isolates of Staphylococcus saprophyticus from human fingertips, SZ.YL11 and SZ.PL35w, were established through hybrid assembly. Each possesses a single circular chromosome and a circular plasmid, totaling 2,611,553 and 2,611,619 bp, respectively (with G + C 33.14% for both).

SpSIZ1 from hyperaccumulator Sedum plumbizincicola orchestrates SpABI5 to fine-tune cadmium tolerance

Sedum plumbizincicola is a renowned hyperaccumulator of cadmium (Cd), possesses significant potential for eco-friendly phytoremediation of soil contaminated with Cd. Nevertheless, comprehension of the mechanisms underpinning its Cd stress response remains constrained, primarily due to the absence of a comprehensive genome sequence and an established genetic transformation system. In this study, we successfully identified a novel protein that specifically responds to Cd stress through early comparative iTRAQ proteome and transcriptome analyses under Cd stress conditions. To further investigate its structure, we employed AlphaFold, a powerful tool for protein structure prediction, and found that this newly identified protein shares a similar structure with Arabidopsis AtSIZ1. Therefore, we named it Sedum plumbizincicola SIZ1 (SpSIZ1). Our study revealed that SpSIZ1 plays a crucial role in positively regulating Cd tolerance through its coordination with SpABI5. Overexpression of SpSIZ1 significantly enhanced plant resistance to Cd stress and reduced Cd accumulation. Expression pattern analysis revealed higher levels of SpSIZ1 expression in roots compared to stems and leaves, with up-regulation under Cd stress induction. Importantly, overexpressing SpSIZ1 resulted in lower Cd translocation factors (Tfs) but maintained relatively constant Cd levels in roots under Cd stress, leading to enhanced Cd stress resistance in plants. Protein interaction analysis revealed that SpSIZ1 interacts with SpABI5, and the expression of genes responsive to abscisic acid (ABA) through SpABI5-dependent signaling was significantly up-regulated in SpSIZ1-overexpressing plants with Cd stress treatment. Collectively, our results illustrate that SpSIZ1 interacts with SpABI5, enhancing the expression of ABA downstream stress-related genes through SpABI5, thereby increasing Cd tolerance in plants.

AlphaFind: discover structure similarity across the proteome in AlphaFold DB

AlphaFind is a web-based search engine that provides fast structure-based retrieval in the entire set of AlphaFold DB structures. Unlike other protein processing tools, AlphaFind is focused entirely on tertiary structure, automatically extracting the main 3D features of each protein chain and using a machine learning model to find the most similar structures. This indexing approach and the 3D feature extraction method used by AlphaFind have both demonstrated remarkable scalability to large datasets as well as to large protein structures. The web application itself has been designed with a focus on clarity and ease of use. The searcher accepts any valid UniProt ID, Protein Data Bank ID or gene symbol as input, and returns a set of similar protein chains from AlphaFold DB, including various similarity metrics between the query and each of the retrieved results. In addition to the main search functionality, the application provides 3D visualizations of protein structure superpositions in order to allow researchers to instantly analyze the structural similarity of the retrieved results. The AlphaFind web application is available online for free and without any registration at https://alphafind.fi.muni.cz.

Protein research in millets: current status and way forward

MAIN CONCLUSION

Millets' protein studies are lagging behind those of major cereals. Current status and future insights into the investigation of millet proteins are discussed. Millets are important small-seeded cereals majorly grown and consumed by people in Asia and Africa and are considered crops of future food security. Although millets possess excellent climate resilience and nutrient supplementation properties, their research advancements have been lagging behind major cereals. Although considerable genomic resources have been developed in recent years, research on millet proteins and proteomes is currently limited, highlighting a need for further investigation in this area. This review provides the current status of protein research in millets and provides insights to understand protein responses for climate resilience and nutrient supplementation in millets. The reference proteome data is available for sorghum, foxtail millet, and proso millet to date; other millets, such as pearl millet, finger millet, barnyard millet, kodo millet, tef, and browntop millet, do not have any reference proteome data. Many studies were reported on stress-responsive protein identification in foxtail millet, with most studies on the identification of proteins under drought-stress conditions. Pearl millet has a few reports on protein identification under drought and saline stress. Finger millet is the only other millet to have a report on stress-responsive (drought) protein identification in the leaf. For protein localization studies, foxtail millet has a few reports. Sorghum has the highest number of 40 experimentally proven crystal structures, and other millets have fewer or no experimentally proven structures. Further proteomics studies will help dissect the specific proteins involved in climate resilience and nutrient supplementation and aid in breeding better crops to conserve food security.

Review of AlphaFold 3: Transformative Advances in Drug Design and Therapeutics

Google DeepMind Technologies Limited (London, United Kingdom) recently released its new version of the biomolecular structure predictor artificial intelligence (AI) model named AlphaFold 3. Superior in accuracy and more powerful than its predecessor AlphaFold 2, this innovation has astonished the world with its capacity and speed. It takes humans years to determine the structure of various proteins and how the shape works with the receptors but AlphaFold 3 predicts the same structure in seconds. The version's utility is unimaginable in the field of drug discoveries, vaccines, enzymatic processes, and determining the rate and effect of different biological processes. AlphaFold 3 uses similar machine learning and deep learning models such as Gemini (Google DeepMind Technologies Limited). AlphaFold 3 has already established itself as a turning point in the field of computational biochemistry and drug development along with receptor modulation and biomolecular development. With the help of AlphaFold 3 and models similar to this, researchers will gain unparalleled insights into the structural dynamics of proteins and their interactions, opening up new avenues for scientists and doctors to exploit for the benefit of the patient. The integration of AI models like AlphaFold 3, bolstered by rigorous validation against high-standard research publications, is set to catalyze further innovations and offer a glimpse into the future of biomedicine.

Polyglutamine disease proteins: Commonalities and differences in interaction profiles and pathological effects

Currently, nine polyglutamine (polyQ) expansion diseases are known. They include spinocerebellar ataxias (SCA1, 2, 3, 6, 7, 17), spinal and bulbar muscular atrophy (SBMA), dentatorubral-pallidoluysian atrophy (DRPLA), and Huntington's disease (HD). At the root of these neurodegenerative diseases are trinucleotide repeat mutations in coding regions of different genes, which lead to the production of proteins with elongated polyQ tracts. While the causative proteins differ in structure and molecular mass, the expanded polyQ domains drive pathogenesis in all these diseases. PolyQ tracts mediate the association of proteins leading to the formation of protein complexes involved in gene expression regulation, RNA processing, membrane trafficking, and signal transduction. In this review, we discuss commonalities and differences among the nine polyQ proteins focusing on their structure and function as well as the pathological features of the respective diseases. We present insights from AlphaFold-predicted structural models and discuss the biological roles of polyQ-containing proteins. Lastly, we explore reported protein-protein interaction networks to highlight shared protein interactions and their potential relevance in disease development.

A review on structure-function mechanism and signaling pathway of serine/threonine protein PIM kinases as a therapeutic target

The proviral integration for the Moloney murine leukemia virus (PIM) kinases, belonging to serine/threonine kinase family, have been found to be overexpressed in various types of cancers, such as prostate, breast, colon, endometrial, gastric, and pancreatic cancer. The three isoforms PIM kinases i.e., PIM1, PIM2, and PIM3 share a high degree of sequence and structural similarity and phosphorylate substrates controlling tumorigenic phenotypes like proliferation and cell survival. Targeting short-lived PIM kinases presents an intriguing strategy as in vivo knock-down studies result in non-lethal phenotypes, indicating that clinical inhibition of PIM might have fewer adverse effects. The ATP binding site (hinge region) possesses distinctive attributes, which led to the development of novel small molecule scaffolds that target either one or all three PIM isoforms. Machine learning and structure-based approaches have been at the forefront of developing novel and effective chemical therapeutics against PIM in preclinical and clinical settings, and none have yet received approval for cancer treatment. The stability of PIM isoforms is maintained by PIM kinase activity, which leads to resistance against PIM inhibitors and chemotherapy; thus, to overcome such effects, PIM proteolysis targeting chimeras (PROTACs) are now being developed that specifically degrade PIM proteins. In this review, we recapitulate an overview of the oncogenic functions of PIM kinases, their structure, function, and crucial signaling network in different types of cancer, and the potential of pharmacological small-molecule inhibitors. Further, our comprehensive review also provides valuable insights for developing novel antitumor drugs that specifically target PIM kinases in the future. In conclusion, we provide insights into the benefits of degrading PIM kinases as opposed to blocking their catalytic activity to address the oncogenic potential of PIM kinases.

Insights into the Mechanism of Catalytic Activity of Plasmodium Parasite Malate-Quinone Oxidoreductase

Plasmodium malate-quinone oxidoreductase (MQO) is a membrane flavoprotein catalyzing the oxidation of malate to oxaloacetate and the reduction of quinone to quinol. Recently, using a yeast expression system, we demonstrated that MQO, expressed in place of mitochondrial malate dehydrogenase (MDH), contributes to the TCA cycle and the electron transport chain in mitochondria, making MQO attractive as a promising drug target in Plasmodium malaria parasites, which lack mitochondrial MDH. However, there is little information on the structure of MQO and its catalytic mechanism, information that will be required to develop novel drugs. Here, we investigated the catalytic site of P. falciparum MQO (PfMQO) using our yeast expression system. We generated a model structure for PfMQO with the AI tool AlphaFold and used protein footprinting by acetylation with acetic anhydride to analyze the surface topology of the model, confirming the computational prediction to be reasonably accurate. Moreover, a putative catalytic site, which includes a possible flavin-binding site, was identified by this combination of protein footprinting and structural prediction model. This active site was analyzed by site-directed mutagenesis. By measuring enzyme activity and protein expression levels in the PfMQO mutants, we showed that several residues at the active site are essential for enzyme function. In addition, a single substitution mutation near the catalytic site resulted in enhanced sensitivity to ferulenol, an inhibitor of PfMQO that competes with malate for binding to the enzyme. This strongly supports the notion that the substrate binds to the proposed catalytic site. Then, the location of the catalytic site was demonstrated by structural comparison with a homologous enzyme. Finally, we used our results to propose a mechanism for the catalytic activity of MQO by reference to the mechanism of action of structurally or functionally homologous enzymes.

Generative artificial intelligence performs rudimentary structural biology modeling

Natural language-based generative artificial intelligence (AI) has become increasingly prevalent in scientific research. Intriguingly, capabilities of generative pre-trained transformer (GPT) language models beyond the scope of natural language tasks have recently been identified. Here we explored how GPT-4 might be able to perform rudimentary structural biology modeling. We prompted GPT-4 to model 3D structures for the 20 standard amino acids and an α-helical polypeptide chain, with the latter incorporating Wolfram mathematical computation. We also used GPT-4 to perform structural interaction analysis between nirmatrelvir and its target, the SARS-CoV-2 main protease. Geometric parameters of the generated structures typically approximated close to experimental references. However, modeling was sporadically error-prone and molecular complexity was not well tolerated. Interaction analysis further revealed the ability of GPT-4 to identify specific amino acid residues involved in ligand binding along with corresponding bond distances. Despite current limitations, we show the capacity of natural language generative AI to perform basic structural biology modeling and interaction analysis with atomic-scale accuracy.

Plant cell wall-mediated disease resistance: Current understanding and future perspectives

Beyond their function as structural barriers, plant cell walls are essential elements for the adaptation of plants to environmental conditions. Cell walls are dynamic structures whose composition and integrity can be altered in response to environmental challenges and developmental cues. These wall changes are perceived by plant sensors/receptors to trigger adaptative responses during development and upon stress perception. Plant cell wall damage caused by pathogen infection, wounding, or other stresses leads to the release of wall molecules, such as carbohydrates (glycans), that function as damage-associated molecular patterns (DAMPs). DAMPs are perceived by the extracellular ectodomains (ECDs) of pattern recognition receptors (PRRs) to activate pattern-triggered immunity (PTI) and disease resistance. Similarly, glycans released from the walls and extracellular layers of microorganisms interacting with plants are recognized as microbe-associated molecular patterns (MAMPs) by specific ECD-PRRs triggering PTI responses. The number of oligosaccharides DAMPs/MAMPs identified that are perceived by plants has increased in recent years. However, the structural mechanisms underlying glycan recognition by plant PRRs remain limited. Currently, this knowledge is mainly focused on receptors of the LysM-PRR family, which are involved in the perception of various molecules, such as chitooligosaccharides from fungi and lipo-chitooligosaccharides (i.e., Nod/MYC factors from bacteria and mycorrhiza, respectively) that trigger differential physiological responses. Nevertheless, additional families of plant PRRs have recently been implicated in oligosaccharide/polysaccharide recognition. These include receptor kinases (RKs) with leucine-rich repeat and Malectin domains in their ECDs (LRR-MAL RKs), Catharanthus roseus RECEPTOR-LIKE KINASE 1-LIKE group (CrRLK1L) with Malectin-like domains in their ECDs, as well as wall-associated kinases, lectin-RKs, and LRR-extensins. The characterization of structural basis of glycans recognition by these new plant receptors will shed light on their similarities with those of mammalians involved in glycan perception. The gained knowledge holds the potential to facilitate the development of sustainable, glycan-based crop protection solutions.

Inferring molecular inhibition potency with AlphaFold predicted structures

Even though in silico drug ligand-based methods have been successful in predicting interactions with known target proteins, they struggle with new, unassessed targets. To address this challenge, we propose an approach that integrates structural data from AlphaFold 2 predicted protein structures into machine learning models. Our method extracts 3D structural protein fingerprints and combines them with ligand structural data to train a single machine learning model. This model captures the relationship between ligand properties and the unique structural features of various target proteins, enabling predictions for never before tested molecules and protein targets. To assess our model, we used a dataset of 144 Human G-protein Coupled Receptors (GPCRs) with over 140,000 measured inhibition constants (Ki) values. Results strongly suggest that our approach performs as well as state-of-the-art ligand-based methods. In a second modeling approach that used 129 targets for training and a separate test set of 15 different protein targets, our model correctly predicted interactions for 73% of targets, with explained variances exceeding 0.50 in 22% of cases. Our findings further verified that the usage of experimentally determined protein structures produced models that were statistically indistinct from the Alphafold synthetic structures. This study presents a proteo-chemometric drug screening approach that uses a simple and scalable method for extracting protein structural information for usage in machine learning models capable of predicting protein-molecule interactions even for orphan targets.

Automated Flow Peptide Synthesis Enables Engineering of Proteins with Stabilized Transient Binding Pockets

Engineering at the amino acid level is key to enhancing the properties of existing proteins in a desired manner. So far, protein engineering has been dominated by genetic approaches, which have been extremely powerful but only allow for minimal variations beyond the canonical amino acids. Chemical peptide synthesis allows the unrestricted incorporation of a vast set of unnatural amino acids with much broader functionalities, including the incorporation of post-translational modifications or labels. Here we demonstrate the potential of chemical synthesis to generate proteins in a specific conformation, which would have been unattainable by recombinant protein expression. We use recently established rapid automated flow peptide synthesis combined with solid-phase late-stage modifications to rapidly generate a set of FK506-binding protein 51 constructs bearing defined intramolecular lactam bridges. This trapped an otherwise rarely populated transient pocket-as confirmed by crystal structures-which led to an up to 39-fold improved binding affinity for conformation-selective ligands and represents a unique system for the development of ligands for this rare conformation. Overall, our results show how rapid automated flow peptide synthesis can be applied to precision protein engineering.

A journey from molecule to physiology and in silico tools for drug discovery targeting the transient receptor potential vanilloid type 1 (TRPV1) channel

The heat and capsaicin receptor TRPV1 channel is widely expressed in nerve terminals of dorsal root ganglia (DRGs) and trigeminal ganglia innervating the body and face, respectively, as well as in other tissues and organs including central nervous system. The TRPV1 channel is a versatile receptor that detects harmful heat, pain, and various internal and external ligands. Hence, it operates as a polymodal sensory channel. Many pathological conditions including neuroinflammation, cancer, psychiatric disorders, and pathological pain, are linked to the abnormal functioning of the TRPV1 in peripheral tissues. Intense biomedical research is underway to discover compounds that can modulate the channel and provide pain relief. The molecular mechanisms underlying temperature sensing remain largely unknown, although they are closely linked to pain transduction. Prolonged exposure to capsaicin generates analgesia, hence numerous capsaicin analogs have been developed to discover efficient analgesics for pain relief. The emergence of in silico tools offered significant techniques for molecular modeling and machine learning algorithms to indentify druggable sites in the channel and for repositioning of current drugs aimed at TRPV1. Here we recapitulate the physiological and pathophysiological functions of the TRPV1 channel, including structural models obtained through cryo-EM, pharmacological compounds tested on TRPV1, and the in silico tools for drug discovery and repositioning.

Blanket antimicrobial resistance gene database with structural information, BOARDS, provides insights on historical landscape of resistance prevalence and effects of mutations in enzyme structure

Antimicrobial resistance (AMR) in pathogenic bacteria poses a significant threat to public health, yet there is still a need for development in the tools to deeply understand AMR genes based on genetic or structural information. In this study, we present an interactive web database named Blanket Overarching Antimicrobial-Resistance gene Database with Structural information (BOARDS, sbml.unist.ac.kr), a database that comprehensively includes 3,943 reported AMR gene information for 1,997 extended spectrum beta-lactamase (ESBL) and 1,946 other genes as well as a total of 27,395 predicted protein structures. These structures, which include both wild-type AMR genes and their mutants, were derived from 80,094 publicly available whole-genome sequences. In addition, we developed the rapid analysis and detection tool of antimicrobial-resistance (RADAR), a one-stop analysis pipeline to detect AMR genes across whole-genome sequencing (WGSs). By integrating BOARDS and RADAR, the AMR prevalence landscape for eight multi-drug resistant pathogens was reconstructed, leading to unexpected findings such as the pre-existence of the MCR genes before their official reports. Enzymatic structure prediction-based analysis revealed that the occurrence of mutations found in some ESBL genes was found to be closely related to the binding affinities with their antibiotic substrates. Overall, BOARDS can play a significant role in performing in-depth analysis on AMR.IMPORTANCEWhile the increasing antibiotic resistance (AMR) in pathogen has been a burden on public health, effective tools for deep understanding of AMR based on genetic or structural information remain limited. In this study, a blanket overarching antimicrobial-resistance gene database with structure information (BOARDS)-a web-based database that comprehensively collected AMR gene data with predictive protein structural information was constructed. Additionally, we report the development of a RADAR pipeline that can analyze whole-genome sequences as well. BOARDS, which includes sequence and structural information, has shown the historical landscape and prevalence of the AMR genes and can provide insight into single-nucleotide polymorphism effects on antibiotic degrading enzymes within protein structures.

Whole genome structural predictions reveal hidden diversity in putative oxidative enzymes of the lignocellulose-degrading ascomycete Parascedosporium putredinis NO1

IMPORTANCE

An annotated reference genome has revealed P. putredinis NO1 as a useful resource for the identification of new lignocellulose-degrading enzymes for biorefining of woody plant biomass. Utilizing a "structure-omics"-based searching strategy, we identified new potentially lignocellulose-active sequences that would have been missed by traditional sequence searching methods. These new identifications, alongside the discovery of novel enzymatic functions from this underexplored lineage with the recent discovery of a new phenol oxidase that cleaves the main structural β-O-4 linkage in lignin from P. putredinis NO1, highlight the underexplored and poorly represented family Microascaceae as a particularly interesting candidate worthy of further exploration toward the valorization of high value biorenewable products.

Impact of AlphaFold on structure prediction of protein complexes: The CASP15-CAPRI experiment

We present the results for CAPRI Round 54, the 5th joint CASP-CAPRI protein assembly prediction challenge. The Round offered 37 targets, including 14 homodimers, 3 homo-trimers, 13 heterodimers including 3 antibody-antigen complexes, and 7 large assemblies. On average ~70 CASP and CAPRI predictor groups, including more than 20 automatics servers, submitted models for each target. A total of 21 941 models submitted by these groups and by 15 CAPRI scorer groups were evaluated using the CAPRI model quality measures and the DockQ score consolidating these measures. The prediction performance was quantified by a weighted score based on the number of models of acceptable quality or higher submitted by each group among their five best models. Results show substantial progress achieved across a significant fraction of the 60+ participating groups. High-quality models were produced for about 40% of the targets compared to 8% two years earlier. This remarkable improvement is due to the wide use of the AlphaFold2 and AlphaFold2-Multimer software and the confidence metrics they provide. Notably, expanded sampling of candidate solutions by manipulating these deep learning inference engines, enriching multiple sequence alignments, or integration of advanced modeling tools, enabled top performing groups to exceed the performance of a standard AlphaFold2-Multimer version used as a yard stick. This notwithstanding, performance remained poor for complexes with antibodies and nanobodies, where evolutionary relationships between the binding partners are lacking, and for complexes featuring conformational flexibility, clearly indicating that the prediction of protein complexes remains a challenging problem.

Assessment of the Performances of the Protein Modeling Techniques Participating in CASP15 Using a Structure-Based Functional Site Prediction Approach: ResiRole

BACKGROUND

Model quality assessments via computational methods which entail comparisons of the modeled structures to the experimentally determined structures are essential in the field of protein structure prediction. The assessments provide means to benchmark the accuracies of the modeling techniques and to aid with their development. We previously described the ResiRole method to gauge model quality principally based on the preservation of the structural characteristics described in SeqFEATURE functional site prediction models.

METHODS

We apply ResiRole to benchmark modeling group performances in the Critical Assessment of Structure Prediction experiment, round 15. To gauge model quality, a normalized Predicted Functional site Similarity Score (PFSS) was calculated as the average of one minus the absolute values of the differences of the functional site prediction probabilities, as found for the experimental structures versus those found at the corresponding sites in the structure models.

RESULTS

The average PFSS per modeling group (gPFSS) correlates with standard quality metrics, and can effectively be used to rank the accuracies of the groups. For the free modeling (FM) category, correlation coefficients of the Local Distance Difference Test (LDDT) and Global Distance Test-Total Score (GDT-TS) metrics with gPFSS were 0.98239 and 0.87691, respectively. An example finding for a specific group is that the gPFSS for EMBER3D was higher than expected based on the predictive relationship between gPFSS and LDDT. We infer the result is due to the use of constraints imprinted by function that are a part of the EMBER3D methodology. Also, we find functional site predictions that may guide further functional characterizations of the respective proteins.

CONCLUSION

The gPFSS metric provides an effective means to assess and rank the performances of the structure prediction techniques according to their abilities to accurately recount the structural features at predicted functional sites.

Identification of Missense Variants Affecting Carcass Traits for Hanwoo Precision Breeding

This study aimed to identify causal variants associated with important carcass traits such as weight and meat quality in Hanwoo cattle. We analyzed missense mutations extracted from imputed sequence data (ARS-UCD1.2) and performed an exon-specific association test on the carcass traits of 16,970 commercial Hanwoo. We found 33, 2, 1, and 3 significant SNPs associated with carcass weight (CW), backfat thickness (BFT), eye muscle area (EMA), and marbling score (MS), respectively. In CW and EMA, the most significant missense SNP was identified at 19,524,263 on BTA14 and involved the PRKDC. A missense SNP in the ZFAND2B, located at 107,160,304 on BTA2 was identified as being involved in BFT. For MS, missense SNP in the ACVR2B gene, located at 11,849,704 in BTA22 was identified as the most significant marker. The contribution of the most significant missense SNPs to genetic variance was confirmed to be 8.47%, 2.08%, 1.73%, and 1.19% in CW, BFT, EMA, and MS, respectively. We generated favorable and unfavorable haplotype combinations based on the significant SNPs for CW. Significant differences in GEBV (Genomic Estimated Breeding Values) were observed between groups with each favorable and unfavorable haplotype combination. In particular, the missense SNPs in PRKDC, MRPL9, and ANKFN1 appear to significantly affect the protein's function and structure, making them strong candidates as causal mutations. These missense SNPs have the potential to serve as valuable markers for improving carcass traits in Hanwoo commercial farms.

Perspectives of Proteomics in Respiratory Allergic Diseases

Proteomics in respiratory allergic diseases has such a battery of techniques and programs that one would almost think there is nothing impossible to find, invent or mold. All the resources that we document here are involved in solving problems in allergic diseases, both diagnostic and prognostic treatment, and immunotherapy development. The main perspectives, according to this version, are in three strands and/or a lockout immunological system: (1) Blocking the diapedesis of the cells involved, (2) Modifications and blocking of paratopes and epitopes being understood by modifications to antibodies, antagonisms, or blocking them, and (3) Blocking FcεRI high-affinity receptors to prevent specific IgEs from sticking to mast cells and basophils. These tools and targets in the allergic landscape are, in our view, the prospects in the field. However, there are still many allergens to identify, including some homologies between allergens and cross-reactions, through the identification of structures and epitopes. The current vision of using proteomics for this purpose remains a constant; this is also true for the basis of diagnostic and controlled systems for immunotherapy. Ours is an open proposal to use this vision for treatment.

Discovery of novel and selective SIK2 inhibitors by the application of AlphaFold structures and generative models

Salt-inducible kinase 2 (SIK2) has been recognized as a potential target for anti-inflammation and anti-cancer therapy. In this paper, based on the binding pose of the reported compound (GLPG-3970, 3) with AlphaFold protein structure, a series of hinge cores were generated via AI-generative models (Chemistry42). After the molecular docking, synthesis, and biological evaluation, a hit molecule (7f) targeting SIK2 was obtained with a novel scaffold. Further SAR exploration led to the discovery of compound 8g with superior potency against SIK2 compared with the reported inhibitors. Furthermore, 8g also demonstrated excellent selectivity over other AMPK kinases, favorable in vitro ADMET profiles and decent cellular activities. This work provides an alternative approach to the discovery of novel and selective kinase inhibitors.

Omics and systems view of innate immune pathways

Multiomics approaches to studying systems biology are very powerful techniques that can elucidate changes in the genomic, transcriptomic, proteomic, and metabolomic levels within a cell type in response to an infection. These approaches are valuable for understanding the mechanisms behind disease pathogenesis and how the immune system responds to being challenged. With the emergence of the COVID-19 pandemic, the importance and utility of these tools have become evident in garnering a better understanding of the systems biology within the innate and adaptive immune response and for developing treatments and preventative measures for new and emerging pathogens that pose a threat to human health. In this review, we focus on state-of-the-art omics technologies within the scope of innate immunity.

State-of-the-Art in the Drug Discovery Pathway for Chagas Disease: A Framework for Drug Development and Target Validation

Chagas disease is the most important protozoan infection in the Americas, and constitutes a significant public health concern throughout the world. Development of new medications against its etiologic agent, Trypanosoma cruzi, has been traditionally slow and difficult, lagging in comparison with diseases caused by other kinetoplastid parasites. Among the factors that explain this are the incompletely understood mechanisms of pathogenesis of T. cruzi infection and its complex set of interactions with the host in the chronic stage of the disease. These demand the performance of a variety of in vitro and in vivo assays as part of any drug development effort. In this review, we discuss recent breakthroughs in the understanding of the parasite's life cycle and their implications in the search for new chemotherapeutics. For this, we present a framework to guide drug discovery efforts against Chagas disease, considering state-of-the-art preclinical models and recently developed tools for the identification and validation of molecular targets.

AlphaFold, allosteric, and orthosteric drug discovery: Ways forward

Drug discovery is arguably a highly challenging and significant interdisciplinary aim. The stunning success of the artificial intelligence-powered AlphaFold, whose latest version is buttressed by an innovative machine-learning approach that integrates physical and biological knowledge about protein structures, raised drug discovery hopes that unsurprisingly, have not come to bear. Even though accurate, the models are rigid, including the drug pockets. AlphaFold's mixed performance poses the question of how its power can be harnessed in drug discovery. Here we discuss possible ways of going forward wielding its strengths, while bearing in mind what AlphaFold can and cannot do. For kinases and receptors, an input enriched in active (ON) state models can better AlphaFold's chance of rational drug design success.

Open data and algorithms for open science in AI-driven molecular informatics

Recent years have seen a sharp increase in the development of deep learning and artificial intelligence-based molecular informatics. There has been a growing interest in applying deep learning to several subfields, including the digital transformation of synthetic chemistry, extraction of chemical information from the scientific literature, and AI in natural product-based drug discovery. The application of AI to molecular informatics is still constrained by the fact that most of the data used for training and testing deep learning models are not available as FAIR and open data. As open science practices continue to grow in popularity, initiatives which support FAIR and open data as well as open-source software have emerged. It is becoming increasingly important for researchers in the field of molecular informatics to embrace open science and to submit data and software in open repositories. With the advent of open-source deep learning frameworks and cloud computing platforms, academic researchers are now able to deploy and test their own deep learning models with ease. With the development of new and faster hardware for deep learning and the increasing number of initiatives towards digital research data management infrastructures, as well as a culture promoting open data, open source, and open science, AI-driven molecular informatics will continue to grow. This review examines the current state of open data and open algorithms in molecular informatics, as well as ways in which they could be improved in future.

Computational Approaches Drive Developments in Immune-Oncology Therapies for PD-1/PD-L1 Immune Checkpoint Inhibitors

Computational approaches in immune-oncology therapies focus on using data-driven methods to identify potential immune targets and develop novel drug candidates. In particular, the search for PD-1/PD-L1 immune checkpoint inhibitors (ICIs) has enlivened the field, leveraging the use of cheminformatics and bioinformatics tools to analyze large datasets of molecules, gene expression and protein-protein interactions. Up to now, there is still an unmet clinical need for improved ICIs and reliable predictive biomarkers. In this review, we highlight the computational methodologies applied to discovering and developing PD-1/PD-L1 ICIs for improved cancer immunotherapies with a greater focus in the last five years. The use of computer-aided drug design structure- and ligand-based virtual screening processes, molecular docking, homology modeling and molecular dynamics simulations methodologies essential for successful drug discovery campaigns focusing on antibodies, peptides or small-molecule ICIs are addressed. A list of recent databases and web tools used in the context of cancer and immunotherapy has been compilated and made available, namely regarding a general scope, cancer and immunology. In summary, computational approaches have become valuable tools for discovering and developing ICIs. Despite significant progress, there is still a need for improved ICIs and biomarkers, and recent databases and web tools have been compiled to aid in this pursuit.

Computational prediction of protein folding rate using structural parameters and network centrality measures

Protein folding is a complex physicochemical process whereby a polymer of amino acids samples numerous conformations in its unfolded state before settling on an essentially unique native three-dimensional (3D) structure. To understand this process, several theoretical studies have used a set of 3D structures, identified different structural parameters, and analyzed their relationships using the natural logarithmic protein folding rate (ln(k_f)). Unfortunately, these structural parameters are specific to a small set of proteins that are not capable of accurately predicting ln(k_f) for both two-state (TS) and non-two-state (NTS) proteins. To overcome the limitations of the statistical approach, a few machine learning (ML)-based models have been proposed using limited training data. However, none of these methods can explain plausible folding mechanisms. In this study, we evaluated the predictive capabilities of ten different ML algorithms using eight different structural parameters and five different network centrality measures based on newly constructed datasets. In comparison to the other nine regressors, support vector machine was found to be the most appropriate for predicting ln(k_f) with mean absolute differences of 1.856, 1.55, and 1.745 for the TS, NTS, and combined datasets, respectively. Furthermore, combining structural parameters and network centrality measures improves the prediction performance compared to individual parameters, indicating that multiple factors are involved in the folding process.

Before and after AlphaFold2: An overview of protein structure prediction

Three-dimensional protein structure is directly correlated with its function and its determination is critical to understanding biological processes and addressing human health and life science problems in general. Although new protein structures are experimentally obtained over time, there is still a large difference between the number of protein sequences placed in Uniprot and those with resolved tertiary structure. In this context, studies have emerged to predict protein structures by methods based on a template or free modeling. In the last years, different methods have been combined to overcome their individual limitations, until the emergence of AlphaFold2, which demonstrated that predicting protein structure with high accuracy at unprecedented scale is possible. Despite its current impact in the field, AlphaFold2 has limitations. Recently, new methods based on protein language models have promised to revolutionize the protein structural biology allowing the discovery of protein structure and function only from evolutionary patterns present on protein sequence. Even though these methods do not reach AlphaFold2 accuracy, they already covered some of its limitations, being able to predict with high accuracy more than 200 million proteins from metagenomic databases. In this mini-review, we provide an overview of the breakthroughs in protein structure prediction before and after AlphaFold2 emergence.

Molecular Identification and In Silico Protein Analysis of a Novel BCOR-CLGN Gene Fusion in Intrathoracic BCOR-Rearranged Sarcoma

BCOR (BCL6 corepressor)-rearranged sarcomas (BRSs) are a heterogeneous group of sarcomas previously classified as part of the group of "atypical Ewing" or "Ewing-like" sarcomas, without the prototypical ESWR1 gene translocation. Due to their similar morphology and histopathological features, diagnosis is challenging. The most common genetic aberrations are BCOR-CCNB3 fusion and BCOR internal tandem duplication (ITD). Recently, various new fusion partners of BCOR have been documented, such as MAML3, ZC3H7B, RGAG1, and KMT2D, further increasing the complexity of such tumor entities, although the molecular pathogenetic mechanism remains to be elucidated. Here, we present an index case of intrathoracic BRS that carried a novel BCOR-CLGN (calmegin) gene fusion, exhibited by a 52-year-old female diagnosed initially by immunohistochemistry due to the positivity of a BCOR stain; the fusion was identified by next-generation sequencing and was confirmed by Sanger sequencing. In silico protein analysis was performed to demonstrate the 3D structure of the chimera protein. The physicochemical properties of the fusion protein sequence were calculated using the ProtParam web-server tool. Our finding further broadens the fusion partner gene spectrum of BRS. Due to the heterogeneity, molecular ancillary tests serve as powerful tools to discover these unusual variants, and an in silico analysis of the fusion protein offers an appropriate approach toward understanding the exact pathogenesis of such a rare variant.