ConSurf: an algorithmic tool for the identification of functional regions in proteins by surface mapping of phylogenetic information

Genetic variants in the LRP5 gene associated with gain and loss of bone mineral density

The low-density lipoprotein receptor-related protein 5 (LRP5) plays a pivotal role in bone formation, influencing the proliferation and differentiation of osteoblasts and thereby impacting overall bone mass. Genetic variations stemming from non-synonymous single nucleotide polymorphisms (nsSNPs) within the LRP5 gene can lead to either enhanced or diminished function of the resultant protein, culminating in distinct phenotypic expressions such as osteoporosis-pseudoglioma syndrome (OPPG) and high bone mass (HBM). Through in silico analysis of 17 identified nsSNPs, it was observed that 14 of these variants induced damage at highly conserved sites, resulting in the destabilization of both protein function and structure. Notably, the functional alteration, be it a gain or loss, is primarily dictated by the interaction between the molecule and LRP5, rather than the specific amino acid substitution. This research offers an identification of detrimental nsSNPs within the LRP5 protein and serves as a foundation for population-based investigations into the phenotypic repercussions on a broader scale.

Developing the trehalose biosynthesis pathway as an antifungal drug target

Invasive fungal infections are responsible for millions of deaths worldwide each year. Therefore, focusing on innovative approaches to developing therapeutics that target fungal pathogens is critical. Here, we discuss targeting the fungal trehalose biosynthesis pathway with antifungal therapeutics, which may lead to the improvement of human health globally, especially as fungal pathogens continue to emerge due to fluctuations in the climate.

BLTP3A is associated with membranes of the late endocytic pathway and is an effector of CASM

Recent studies have identified a family of rod-shaped proteins thought to mediate lipid transfer at intracellular membrane contacts by a bridge-like mechanism. We show one such protein, bridge-like lipid transfer protein 3A (BLTP3A)/UHRF1BP1 binds VAMP7 vesicles via its C-terminal region and anchors them to lysosomes via its chorein domain containing N-terminal region to Rab7. Upon lysosome damage, BLTP3A-positive vesicles rapidly (within minutes) dissociate from lysosomes. Lysosome damage is known to activate the CASM (Conjugation of ATG8 to Single Membranes) pathway leading to lipidation and recruitment to lysosomes of mammalian ATG8 (mATG8) proteins. We find that this process drives the reassociation of BLTP3A with damaged lysosomes via an interaction of its LIR motif with mATG8 which coincides with a dissociation from the vesicles. Our findings reveal that BLTP3A is an effector of CASM, potentially as part of a mechanism to help repair or minimize lysosome damage.

A p.N92K variant of the GTPase RAC3 disrupts cortical neuron migration and axon elongation

RAC3 encodes a small GTPase of the Rho family, crucial for actin cytoskeleton organization and signaling pathways. De novo deleterious variants in RAC3 cause neurodevelopmental disorder with structural brain anomalies and dysmorphic facies (NEDBAF). Disease-causing variants thus far reported are thought to impact key conserved regions within RAC3, such as the P-loop, switch I/II, and G boxes, which are essential for the interaction with regulatory proteins and effectors. Recently, however, a novel variant, c.276T > A, p.N92K, was identified in a prenatal case with complex brain malformations. This variant, located outside the core functional regions, represents a unique class of RAC3 pathogenic mutations. We investigated the variant's effects using in vitro, in silico, and in vivo approaches. Overexpression of RAC3-N92K in primary hippocampal neurons impaired differentiation, leading to round cell shape with lamellipodia, suggesting that RAC3-N92K is active. Biochemical studies showed that RAC3-N92K is (1) resistant to GAP-mediated inactivation, (2) responsive to GEF activation, and (3) capable of interacting with RAC effectors PAK1 and MLK2, as well as Rho-kinase 1, activating gene expression through SRF, NFκB, and AP1 pathways. Structural analyses suggest that N92K disrupts GAP interactions but preserves interactions with GEF, PAK1, and MLK2. In vivo, RAC3-N92K expression in embryonic mouse cortical neurons led to migration defects and periventricular clustering during corticogenesis, along with impaired axon elongation. These findings indicate that RAC3-N92K's activated state significantly disrupts cortical development, expanding the genetic and pathophysiological spectrum of NEDBAF.

Terminator: A Software Package for Fast and Local Optimization of His-Tag Placement for Protein Affinity Purification

Although the use of affinity tags can greatly improve purification of expressed enzymes, the placement of affinity tags can significantly impact the expression, solubility, and function of recombinant proteins. To facilitate the optimal design of 6xHis-tagged constructs for protein purification, we developed Terminator, a Python-based software package, which takes a UniProt ID or existing protein sequence as input, identifies related sequences, maps sequence conservation retrieved from ConSurf onto protein 3D structures retrieved from the PDB and SWISS-MODEL, and analyzes proximity to cavities and functional sites to recommend the N- or C-terminus for placement of 6xHis fusion tags <15 residues in length. The package also outputs a document with available purification and activity literature for the target and closely related proteins organized by year. Comparative analysis of Terminator predictions against published experimental tag behavior for 6xHis fusion tags <15 residues in length demonstrates an 86-100% accuracy in predicting the relative risk of ill effects between termini and a 92-93% accuracy in predicting the absolute risk of modifying individual termini. This reliability of Terminator's analysis suggests that proximity to surface cavities, not burial of wild-type termini, is the most reliable predictor of ill effects arising from short 6xHis fusion tags. This tool aims to expedite construct design and enhance the successful production of well-behaved proteins for studies in enzymology and biocatalysis with minimal need for computational resources, programming knowledge, or familiarity with protein-tag interference mechanisms.

Sam-Sam Association Between EphA2 and SASH1: In Silico Studies of Cancer-Linked Mutations

Recently, SASH1 has emerged as a novel protein interactor of a few Eph tyrosine kinase receptors like EphA2. These interactions involve the first N-terminal Sam (sterile alpha motif) domain of SASH1 (SASH1-Sam1) and the Sam domain of Eph receptors. Currently, the functional meaning of the SASH1-Sam1/EphA2-Sam complex is unknown, but EphA2 is a well-established and crucial player in cancer onset and progression. Thus, herein, to investigate a possible correlation between the formation of the SASH1-Sam1/EphA2-Sam complex and EphA2 activity in cancer, cancer-linked mutations in SASH1-Sam1 were deeply analyzed. Our research plan relied first on searching the COSMIC database for cancer-related SASH1 variants carrying missense mutations in the Sam1 domain and then, through a variety of bioinformatic tools and molecular dynamic simulations, studying how these mutations could affect the stability of SASH1-Sam1 alone, leading eventually to a defective fold. Next, through docking studies, with the support of AlphaFold2 structure predictions, we investigated if/how mutations in SASH1-Sam1 could affect binding to EphA2-Sam. Our study, apart from presenting a solid multistep research protocol to analyze structural consequences related to cancer-associated protein variants with the support of cutting-edge artificial intelligence tools, suggests a few mutations that could more likely modulate the interaction between SASH1-Sam1 and EphA2-Sam.

In Silico Strategies for Characterizing Inner Cavities of Lipid-Binding Proteins

Cavities in proteins perform diverse functions such as substrate binding, enzyme catalysis, passage for transportation of small molecules, and protein oligomerization. Often, the physical properties of these cavities are closely linked to the protein function; such as the hydrophobic lipid-binding cavities in lipid-binding proteins (LBPs) that protect lipid substrates from the larger aqueous milieu. Therefore, the characterization of protein cavities can provide valuable insights into protein structure-function relationships, hinting toward their mechanism of action while aiding in the identification of ligand binding sites that are essential for drug discovery approaches. Several algorithms have historically been designed to identify and characterize the different types of cavities in protein structures. We summarize these algorithms and provide a step-by-step guide for locating and characterizing internal cavities in proteins using CICLOP by using ATP-binding cassette transporter A1 (ABCA1) as an example.

An evaluation of the precision of computational methods used in drug development initiatives

Computational approaches are commonly employed to expedite and provide decision-making for the drug development process. Drug development programs that involve targets without known crystal structures can be quite challenging. In many cases, a viable approach is to generate reliable homology models using the amino acid sequence of the target. This is followed by a series of validation steps, druggable pocket detection, and then moving forward with lead identification and validation. This study commenced by conducting an initial benchmark exercise using a series of computationally designed sequences for steroid-binding proteins. By conducting an unbiased comparison with the released X-ray crystal structures, the homology models that were generated demonstrated reliable outcomes. The aligned homology models showed a root mean square deviation (RMSD) of less than 0.6 Å when compared to the corresponding X-ray structures. Three different methods were used to detect the druggable cavities for comparison, and the identified pockets closely resembled those of the crystal structures. The achievement of near-native pose prediction was made possible by utilizing the comprehensive binding energy function that characterizes the interaction between each pose and the neighboring residues. In order to address the issue of limited correlation between entropy and internal energy in docking, an alternative was devised by incorporating entropy as a post-docking optimization step to enhance the accuracy of ligand binding affinity predictions and improve the overall quality of the results.

Comparative evaluation of methods for the prediction of protein-ligand binding sites

The accurate identification of protein-ligand binding sites is of critical importance in understanding and modulating protein function. Accordingly, ligand binding site prediction has remained a research focus for over three decades with over 50 methods developed and a change of paradigm from geometry-based to machine learning. In this work, we collate 13 ligand binding site predictors, spanning 30 years, focusing on the latest machine learning-based methods such as VN-EGNN, IF-SitePred, GrASP, PUResNet, and DeepPocket and compare them to the established P2Rank, PRANK and fpocket and earlier methods like PocketFinder, Ligsite and Surfnet. We benchmark the methods against the human subset of our new curated reference dataset, LIGYSIS. LIGYSIS is a comprehensive protein-ligand complex dataset comprising 30,000 proteins with bound ligands which aggregates biologically relevant unique protein-ligand interfaces across biological units of multiple structures from the same protein. LIGYSIS is an improvement for testing methods over earlier datasets like sc-PDB, PDBbind, binding MOAD, COACH420 and HOLO4K which either include 1:1 protein-ligand complexes or consider asymmetric units. Re-scoring of fpocket predictions by PRANK and DeepPocket display the highest recall (60%) whilst IF-SitePred presents the lowest recall (39%). We demonstrate the detrimental effect that redundant prediction of binding sites has on performance as well as the beneficial impact of stronger pocket scoring schemes, with improvements up to 14% in recall (IF-SitePred) and 30% in precision (Surfnet). Finally, we propose top-N+2 recall as the universal benchmark metric for ligand binding site prediction and urge authors to share not only the source code of their methods, but also of their benchmark.Scientific contributionsThis study conducts the largest benchmark of ligand binding site prediction methods to date, comparing 13 original methods and 15 variants using 10 informative metrics. The LIGYSIS dataset is introduced, which aggregates biologically relevant protein-ligand interfaces across multiple structures of the same protein. The study highlights the detrimental effect of redundant binding site prediction and demonstrates significant improvement in recall and precision through stronger scoring schemes. Finally, top-N+2 recall is proposed as a universal benchmark metric for ligand binding site prediction, with a recommendation for open-source sharing of both methods and benchmarks.

Virulence-linked adhesin drives mutualist colonization of the bee gut via biofilm formation

Bacterial biofilms are stable multicellular structures that can enable long term host association. Yet, the role of biofilms in supporting gut mutualism is still not fully understood. Here, we investigate Snodgrassella alvi, a beneficial bacterial symbiont of honey bees, and find that biofilm formation is required for its colonization of the bee gut. We constructed fifteen S. alvi mutants containing knockouts of genes known to promote colonization with putative roles in biofilm formation. Genes required for colonization included staA and staB, encoding trimeric autotransporter adhesins (TAAs) and mltA, encoding a lytic transglycosylase. Intriguingly, TAAs are considered virulence factors in pathogens but support mutualism by the symbiont S. alvi. In vitro, biofilm formation was reduced in ΔstaB cells and abolished in the other two mutants. Loss of staA also reduced auto-aggregation and cell-cell connections. Based on structural predictions, StaA/B are massive (>300 nm) TAAs with many repeats in their stalk regions. Further, we find that StaA/B are conserved across Snodgrassella species, suggesting that StaA/B-dependent colonization is characteristic of this symbiont lineage. Finally, staA deletion increases sensitivity to bactericidal antimicrobials, suggesting that the biofilm indirectly buffers against antibiotic stress. In all, the inability of two biofilm-deficient strains (ΔstaA and ΔmltA) to effectively mono-colonize bees indicates that S. alvi biofilm formation is required for colonization of the bee gut. We envision the bee gut system as a genetically tractable model for studying the physical basis of biofilm-mutualist-gut interactions.

Proton-triggered rearrangement of the AMPA receptor N-terminal domains impacts receptor kinetics and synaptic localization

AMPA glutamate receptors (AMPARs) are ion channel tetramers that mediate the majority of fast excitatory synaptic transmission. They are composed of four subunits (GluA1-GluA4); the GluA2 subunit dominates AMPAR function throughout the forebrain. Its extracellular N-terminal domain (NTD) determines receptor localization at the synapse, ensuring reliable synaptic transmission and plasticity. This synaptic anchoring function requires a compact NTD tier, stabilized by a GluA2-specific NTD interface. Here we show that low pH conditions, which accompany synaptic activity, rupture this interface. All-atom molecular dynamics simulations reveal that protonation of an interfacial histidine residue (H208) centrally contributes to NTD rearrangement. Moreover, in stark contrast to their canonical compact arrangement at neutral pH, GluA2 cryo-electron microscopy structures exhibit a wide spectrum of NTD conformations under acidic conditions. We show that the consequences of this pH-dependent conformational control are twofold: rupture of the NTD tier slows recovery from desensitized states and increases receptor mobility at mouse hippocampal synapses. Therefore, a proton-triggered NTD switch will shape both AMPAR location and kinetics, thereby impacting synaptic signal transmission.

Advances in the Application of Protein Language Modeling for Nucleic Acid Protein Binding Site Prediction

Protein and nucleic acid binding site prediction is a critical computational task that benefits a wide range of biological processes. Previous studies have shown that feature selection holds particular significance for this prediction task, making the generation of more discriminative features a key area of interest for many researchers. Recent progress has shown the power of protein language models in handling protein sequences, in leveraging the strengths of attention networks, and in successful applications to tasks such as protein structure prediction. This naturally raises the question of the applicability of protein language models in predicting protein and nucleic acid binding sites. Various approaches have explored this potential. This paper first describes the development of protein language models. Then, a systematic review of the latest methods for predicting protein and nucleic acid binding sites is conducted by covering benchmark sets, feature generation methods, performance comparisons, and feature ablation studies. These comparisons demonstrate the importance of protein language models for the prediction task. Finally, the paper discusses the challenges of protein and nucleic acid binding site prediction and proposes possible research directions and future trends. The purpose of this survey is to furnish researchers with actionable suggestions for comprehending the methodologies used in predicting protein-nucleic acid binding sites, fostering the creation of protein-centric language models, and tackling real-world obstacles encountered in this field.

Rheostatic contributions to protein stability can obscure a position's functional role

Rheostat positions, which can be substituted with various amino acids to tune protein function across a range of outcomes, are a developing area for advancing personalized medicine and bioengineering. Current methods cannot accurately predict which proteins contain rheostat positions or their substitution outcomes. To compare the prevalence of rheostat positions in homologs, we previously investigated their occurrence in two pyruvate kinase (PYK) isozymes. Human liver PYK contained numerous rheostat positions that tuned the apparent affinity for the substrate phosphoenolpyruvate (Kapp-PEP) across a wide range. In contrast, no functional rheostat positions were identified in Zymomonas mobilis PYK (ZmPYK). Further, the set of ZmPYK substitutions included an unusually large number that lacked measurable activity. We hypothesized that the inactive substitution variants had reduced protein stability, precluding detection of Kapp-PEP tuning. Using modified buffers, robust enzymatic activity was obtained for 19 previously-inactive ZmPYK substitution variants at three positions. Surprisingly, both previously-inactive and previously-active substitution variants all had Kapp-PEP values close to wild-type. Thus, none of the three positions were functional rheostat positions, and, unlike human liver PYK, ZmPYK's Kapp-PEP remained poorly tunable by single substitutions. To directly assess effects on stability, we performed thermal denaturation experiments for all ZmPYK substitution variants. Many diminished stability, two enhanced stability, and the three positions showed different thermal sensitivity to substitution, with one position acting as a "stability rheostat." The differences between the two PYK homologs raises interesting questions about the underlying mechanism(s) that permit functional tuning by single substitutions in some proteins but not in others.

The ABCF proteins in Escherichia coli individually cope with 'hard-to-translate' nascent peptide sequences

Organisms possess a wide variety of proteins with diverse amino acid sequences, and their synthesis relies on the ribosome. Empirical observations have led to the misconception that ribosomes are robust protein factories, but in reality, they have several weaknesses. For instance, ribosomes stall during the translation of the proline-rich sequences, but the elongation factor EF-P assists in synthesizing proteins containing the poly-proline sequences. Thus, living organisms have evolved to expand the translation capability of ribosomes through the acquisition of translation elongation factors. In this study, we have revealed that Escherichia coli ATP-Binding Cassette family-F (ABCF) proteins, YheS, YbiT, EttA and Uup, individually cope with various problematic nascent peptide sequences within the exit tunnel. The correspondence between noncanonical translations and ABCFs was YheS for the translational arrest by nascent SecM, YbiT for poly-basic sequence-dependent stalling and poly-acidic sequence-dependent intrinsic ribosome destabilization (IRD), EttA for IRD at the early stage of elongation, and Uup for poly-proline-dependent stalling. Our results suggest that ATP hydrolysis-coupled structural rearrangement and the interdomain linker sequence are pivotal for handling 'hard-to-translate' nascent peptides. Our study highlights a new aspect of ABCF proteins to reduce the potential risks that are encoded within the nascent peptide sequences.

Targeting ATP-binding site of WRN Helicase: Identification of novel inhibitors through pocket analysis and Molecular Dynamics-Enhanced virtual screening

WRN helicase is a critical protein involved in maintaining genomic stability, utilizing ATP hydrolysis to dissolve DNA secondary structures. It has been identified as a promising synthetic lethal target for microsatellite instable (MSI) cancers. However, few WRN helicase inhibitors have been discovered, and their potential binding sites remain unexplored. In this study, we analyzed potential binding sites for WRN inhibitors and focused on the ATP-binding site for screening new inhibitors. Through molecular dynamics-enhanced virtual screening, we identified two compounds, h6 and h15, which effectively inhibited WRN's helicase and ATPase activity in vitro. Importantly, these compounds selectively targeted WRN's ATPase activity, setting them apart from other non-homologous proteins with ATPase activity. In comparison to the homologous protein BLM, h6 exhibits some degree of selectivity towards WRN. We also investigated the binding mode of these compounds to WRN's ATP-binding sites. These findings offer a promising strategy for discovering new WRN inhibitors and present two novel scaffolds, which might be potential for the development of MSI cancer treatment.

Conserved proline residues prevent dimerization and aggregation in the β-lactamase BlaC

Evolution leads to conservation of amino acid residues in protein families. Conserved proline residues are usually considered to ensure the correct folding and to stabilize the three-dimensional structure. Surprisingly, proline residues that are highly conserved in class A β-lactamases were found to tolerate various substitutions without large losses in enzyme activity. We investigated the roles of three conserved prolines at positions 107, 226, and 258 in the β-lactamase BlaC from Mycobacterium tuberculosis and found that mutations can lead to dimerization of the enzyme and an overall less stable protein that is prone to aggregate over time. For the variant Pro107Thr, the crystal structure shows dimer formation resembling domain swapping. It is concluded that the proline substitutions loosen the structure, enhancing multimerization. Even though the enzyme does not lose its properties without the conserved proline residues, the prolines ensure the long-term structural integrity of the enzyme.

Biochemical and structural characterization reveals Rv3400 codes for β-phosphoglucomutase in Mycobacterium tuberculosis

Mycobacterium tuberculosis (Mtb) adapt to various host environments and utilize a variety of sugars and lipids as carbon sources. Among these sugars, maltose and trehalose, also play crucial role in bacterial physiology and virulence. However, some key enzymes involved in trehalose and maltose metabolism in Mtb are not yet known. Here we structurally and functionally characterized a conserved hypothetical gene Rv3400. We determined the crystal structure of Rv3400 at 1.7 Å resolution. The crystal structure revealed that Rv3400 adopts Rossmann fold and shares high structural similarity with haloacid dehalogenase family of proteins. Our comparative structural analysis suggested that Rv3400 could perform either phosphatase or pyrophosphatase or β-phosphoglucomutase (β-PGM) activity. Using biochemical studies, we further confirmed that Rv3400 performs β-PGM activity and hence, Rv3400 encodes for β-PGM in Mtb. Our data also confirm that Mtb β-PGM is a metal dependent enzyme having broad specificity for divalent metal ions. β-PGM converts β-D-glucose-1-phosphate to β-D-glucose-6-phosphate which is required for the generation of ATP and NADPH through glycolysis and pentose phosphate pathway, respectively. Using site directed mutagenesis followed by biochemical studies, we show that two Asp residues in the highly conserved DxD motif, D29 and D31, are crucial for enzyme activity. While D29A, D31A, D29E, D31E and D29N mutants lost complete activity, D31N mutant retained about 30% activity. This study further helps in understanding the role of β-PGM in the physiology of Mtb.

Targeting DCAF5 suppresses SMARCB1-mutant cancer by stabilizing SWI/SNF

Whereas oncogenes can potentially be inhibited with small molecules, the loss of tumour suppressors is more common and is problematic because the tumour-suppressor proteins are no longer present to be targeted. Notable examples include SMARCB1-mutant cancers, which are highly lethal malignancies driven by the inactivation of a subunit of SWI/SNF (also known as BAF) chromatin-remodelling complexes. Here, to generate mechanistic insights into the consequences of SMARCB1 mutation and to identify vulnerabilities, we contributed 14 SMARCB1-mutant cell lines to a near genome-wide CRISPR screen as part of the Cancer Dependency Map Project1-3. We report that the little-studied gene DDB1-CUL4-associated factor 5 (DCAF5) is required for the survival of SMARCB1-mutant cancers. We show that DCAF5 has a quality-control function for SWI/SNF complexes and promotes the degradation of incompletely assembled SWI/SNF complexes in the absence of SMARCB1. After depletion of DCAF5, SMARCB1-deficient SWI/SNF complexes reaccumulate, bind to target loci and restore SWI/SNF-mediated gene expression to levels that are sufficient to reverse the cancer state, including in vivo. Consequently, cancer results not from the loss of SMARCB1 function per se, but rather from DCAF5-mediated degradation of SWI/SNF complexes. These data indicate that therapeutic targeting of ubiquitin-mediated quality-control factors may effectively reverse the malignant state of some cancers driven by disruption of tumour suppressor complexes.

EquiPNAS: improved protein-nucleic acid binding site prediction using protein-language-model-informed equivariant deep graph neural networks

Protein language models (pLMs) trained on a large corpus of protein sequences have shown unprecedented scalability and broad generalizability in a wide range of predictive modeling tasks, but their power has not yet been harnessed for predicting protein-nucleic acid binding sites, critical for characterizing the interactions between proteins and nucleic acids. Here, we present EquiPNAS, a new pLM-informed E(3) equivariant deep graph neural network framework for improved protein-nucleic acid binding site prediction. By combining the strengths of pLM and symmetry-aware deep graph learning, EquiPNAS consistently outperforms the state-of-the-art methods for both protein-DNA and protein-RNA binding site prediction on multiple datasets across a diverse set of predictive modeling scenarios ranging from using experimental input to AlphaFold2 predictions. Our ablation study reveals that the pLM embeddings used in EquiPNAS are sufficiently powerful to dramatically reduce the dependence on the availability of evolutionary information without compromising on accuracy, and that the symmetry-aware nature of the E(3) equivariant graph-based neural architecture offers remarkable robustness and performance resilience. EquiPNAS is freely available at https://github.com/Bhattacharya-Lab/EquiPNAS.

Empowering Protein Engineering through Recombination of Beneficial Substitutions

Directed evolution stands as a seminal technology for generating novel protein functionalities, a cornerstone in biocatalysis, metabolic engineering, and synthetic biology. Today, with the development of various mutagenesis methods and advanced analytical machines, the challenge of diversity generation and high-throughput screening platforms is largely solved, and one of the remaining challenges is: how to empower the potential of single beneficial substitutions with recombination to achieve the epistatic effect. This review overviews experimental and computer-assisted recombination methods in protein engineering campaigns. In addition, integrated and machine learning-guided strategies were highlighted to discuss how these recombination approaches contribute to generating the screening library with better diversity, coverage, and size. A decision tree was finally summarized to guide the further selection of proper recombination strategies in practice, which was beneficial for accelerating protein engineering.

Role of two modules controlling the interaction between SKAP1 and SRC kinases comparison with SKAP2 architecture and consequences for evolution

SRC kinase associated phosphoprotein 1 (SKAP1), an adaptor for protein assembly, plays an important role in the immune system such as stabilizing immune synapses. Understanding how these functions are controlled at the level of the protein-protein interactions is necessary to describe these processes and to develop therapeutics. Here, we dissected the SKAP1 modular organization to recognize SRC kinases and compared it to that of its paralog SRC kinase associated phosphoprotein 2 (SKAP2). Different conserved motifs common to either both proteins or specific to SKAP2 were found using this comparison. Two modules harboring different binding properties between SKAP1 and SKAP2 were identified: one composed of two conserved motifs located in the second interdomain interacting at least with the SH2 domain of SRC kinases and a second one composed of the DIM domain modulated by the SH3 domain and the activation of SRC kinases. This work suggests a convergent evolution of the binding properties of some SRC kinases interacting specifically with either SKAP1 or SKAP2.

Developmental and epileptic encephalopathy 82 (DEE82) with novel compound heterozygous mutations of GOT2 gene

PURPOSE

Developmental and Epileptic Encephalopathies (DEEs) are rare neurological disorders characterized by early-onset medically resistant epileptic seizures, structural brain malformations, and severe developmental delays. These disorders can arise from mutations in genes involved in vital metabolic pathways, including those within the brain. Recent studies have implicated defects in the mitochondrial malate aspartate shuttle (MAS) as potential contributors to the clinical manifestation of infantile epileptic encephalopathy. Although rare, mutations in MDH1, MDH2, AGC1, or GOT2 genes have been reported in patients exhibiting neurological symptoms such as global developmental delay, epilepsy, and progressive microcephaly.

METHOD

In this study, we employed exome data analysis of a patient diagnosed with DEE, focusing on the screening of 1896 epilepsy-related genes listed in the HPO and ClinVar databases. Sanger sequencing was subsequently conducted to validate and assess the inheritance pattern of the identified variants within the family. The evolutionary conservation scores of the mutated residues were evaluated using the ConSurf Database. Furthermore, the impacts of the causative variations on protein stability were analyzed through I-Mutant and MuPro bioinformatic tools. Structural comparisons between wild-type and mutant proteins were performed using PyMOL, and the physicochemical effects of the mutations were assessed using Project Hope.

RESULTS

Exome data analysis unveiled the presence of novel compound heterozygous mutations in the GOT2 gene coding for mitochondrial glutamate aspartate transaminase. Sanger sequencing confirmed the paternal inheritance of the p.Asp257Asn mutation and the maternal inheritance of the p.Arg262Cys mutation. The affected individual exhibited plasma metabolic disturbances, including hyperhomocysteinemia, hyperlactatemia, and reduced levels of methionine and arginine. Detailed bioinformatic analysis indicated that the mutations were located within evolutionarily conserved domains of the enzyme, resulting in disruptions to protein stability and structure.

CONCLUSION

Herein, we describe a case with DEE82 (MIM: # 618721) with pathologic novel biallelic mutations in the GOT2 gene. Early genetic diagnosis of metabolic epilepsies is crucial for long-term neurodevelopmental improvements and seizure control as targeted treatments can be administered based on the affected metabolic pathways.

Identification and In-Silico study of non-synonymous functional SNPs in the human SCN9A gene

Single nucleotide polymorphisms are the most common form of DNA alterations at the level of a single nucleotide in the genomic sequence. Genome-wide association studies (GWAS) were carried to identify potential risk genes or genomic regions by screening for SNPs associated with disease. Recent studies have shown that SCN9A comprises the NaV1.7 subunit, Na+ channels have a gene encoding of 1988 amino acids arranged into 4 domains, all with 6 transmembrane regions, and are mainly found in dorsal root ganglion (DRG) neurons and sympathetic ganglion neurons. Multiple forms of acute hypersensitivity conditions, such as primary erythermalgia, congenital analgesia, and paroxysmal pain syndrome have been linked to polymorphisms in the SCN9A gene. Under this study, we utilized a variety of computational tools to explore out nsSNPs that are potentially damaging to heath by modifying the structure or activity of the SCN9A protein. Over 14 potentially damaging and disease-causing nsSNPs (E1889D, L1802P, F1782V, D1778N, C1370Y, V1311M, Y1248H, F1237L, M936V, I929T, V877E, D743Y, C710W, D623H) were identified by a variety of algorithms, including SNPnexus, SNAP-2, PANTHER, PhD-SNP, SNP & GO, I-Mutant, and ConSurf. Homology modeling, structure validation, and protein-ligand interactions also were performed to confirm 5 notable substitutions (L1802P, F1782V, D1778N, V1311M, and M936V). Such nsSNPs may become the center of further studies into a variety of disorders brought by SCN9A dysfunction. Using in-silico strategies for assessing SCN9A genetic variations will aid in organizing large-scale investigations and developing targeted therapeutics for disorders linked to these variations.

A foundational atlas of autism protein interactions reveals molecular convergence

Translating high-confidence (hc) autism spectrum disorder (ASD) genes into viable treatment targets remains elusive. We constructed a foundational protein-protein interaction (PPI) network in HEK293T cells involving 100 hcASD risk genes, revealing over 1,800 PPIs (87% novel). Interactors, expressed in the human brain and enriched for ASD but not schizophrenia genetic risk, converged on protein complexes involved in neurogenesis, tubulin biology, transcriptional regulation, and chromatin modification. A PPI map of 54 patient-derived missense variants identified differential physical interactions, and we leveraged AlphaFold-Multimer predictions to prioritize direct PPIs and specific variants for interrogation in Xenopus tropicalis and human forebrain organoids. A mutation in the transcription factor FOXP1 led to reconfiguration of DNA binding sites and altered development of deep cortical layer neurons in forebrain organoids. This work offers new insights into molecular mechanisms underlying ASD and describes a powerful platform to develop and test therapeutic strategies for many genetically-defined conditions.

Structural analysis of the YqeY proteins from Campylobacter jejuni and Vibrio parahaemolyticus

YqeY is a functionally and structurally uncharacterized protein that is ubiquitously expressed in bacteria. To gain structural insights into the function of YqeY, we determined the crystal structures of the Campylobacter jejuni and Vibrio parahaemolyticus YqeY proteins (cjYqeY and vpYqeY, respectively) and analyzed the structural and functional roles of conserved residues via a mutational study. Both cjYqeY and vpYqeY were found to adopt a two-domain structure consisting of an N-terminal four-α-helix domain and a C-terminal three-α-helix domain, with a relatively flexible interdomain orientation. The YqeY structure is unique in its linkage of the two α-helix domains although the C-terminal YqeY domain is structurally homologous to the terminal appendages of glutaminyl-tRNA synthetase and tRNA-dependent amidotransferase. We identified six conserved YqeY residues (Y67, R72, E82, Y89, P91, and G119) and evaluated their roles in protein stability via alanine mutation using a thermal shift assay. Residues Y67, R72, Y89, and P91 were shown to be required to maintain the structural integrity of YqeY. In contrast, residues E82 and G119 were not found to be essential for protein stability and are highly likely to contribute to the biological function of YqeY.

CRAFT: a web-integrated cavity prediction tool based on flow transfer algorithm

Numerous computational methods, including evolutionary-based, energy-based, and geometrical-based methods, are utilized to identify cavities inside proteins. Cavity information aids protein function annotation, drug design, poly-pharmacology, and allosteric site investigation. This article introduces "flow transfer algorithm" for rapid and effective identification of diverse protein cavities through multidimensional cavity scan. Initially, it identifies delimiter and susceptible tetrahedra to establish boundary regions and provide seed tetrahedra. Seed tetrahedron faces are precisely scanned using the maximum circle radius to transfer seed flow to neighboring tetrahedra. Seed flow continues until terminated by boundaries or forbidden faces, where a face is forbidden if the estimated maximum circle radius is less or equal to the user-defined maximum circle radius. After a seed scanning, tetrahedra involved in the flow are clustered to locate the cavity. The CRAFT web interface integrates this algorithm for protein cavity identification with enhanced user control. It supports proteins with cofactors, hydrogens, and ligands and provides comprehensive features such as 3D visualization, cavity physicochemical properties, percentage contribution graphs, and highlighted residues for each cavity. CRAFT can be accessed through its web interface at http://pitools.niper.ac.in/CRAFT , complemented by the command version available at https://github.com/PGlab-NIPER/CRAFT/ .Scientific contribution: Flow transfer algorithm is a novel geometric approach for accurate and reliable prediction of diverse protein cavities. This algorithm employs a distinct concept involving maximum circle radius within the 3D Delaunay triangulation to address diverse van der Waals radii while existing methods overlook atom specific van der Waals radii or rely on complex weighted geometric techniques.

Molecular details of the CPSF73-CPSF100 C-terminal heterodimer and interaction with Symplekin

Eukaryotic pre-mRNA is processed by a large multiprotein complex to accurately cleave the 3' end, and to catalyse the addition of the poly(A) tail. Within this cleavage and polyadenylation specificity factor (CPSF) machinery, the CPSF73/CPSF3 endonuclease subunit directly contacts both CPSF100/CPSF2 and the scaffold protein Symplekin to form a subcomplex known as the core cleavage complex or mammalian cleavage factor. Here we have taken advantage of a stable CPSF73-CPSF100 minimal heterodimer from Encephalitozoon cuniculi to determine the solution structure formed by the first and second C-terminal domain (CTD1 and CTD2) of both proteins. We find a large number of contacts between both proteins in the complex, and notably in the region between CTD1 and CTD2. A similarity is also observed between CTD2 and the TATA-box binding protein (TBP) domains. Separately, we have determined the structure of the terminal CTD3 domain of CPSF73, which also belongs to the TBP domain family and is connected by a flexible linker to the rest of CPSF73. Biochemical assays demonstrate a key role for the CTD3 of CPSF73 in binding Symplekin, and structural models of the trimeric complex from other species allow for comparative analysis and support an overall conserved architecture.

Revealing the DNA Binding Modes of CsoR by EPR Spectroscopy

In pathogens, a unique class of metalloregulator proteins, called gene regulatory proteins, sense specific metal ions that initiate gene transcription of proteins that export metal ions from the cell, thereby preventing toxicity and cell death. CsoR is a metalloregulator protein found in various bacterial systems that "sense" Cu(I) ions with high affinity. Upon copper binding, CsoR dissociates from the DNA promoter region, resulting in initiation of gene transcription. Crystal structures of CsoR in the presence and absence of Cu(I) from various bacterial systems have been reported, suggesting either a dimeric or tetrameric structure of these helical proteins. However, structural information about the CsoR-DNA complex is missing. Here, we applied electron paramagnetic resonance (EPR) spectroscopy to follow the conformational and dynamical changes that Mycobacterium tuberculosis CsoR undergoes upon DNA binding in solution. We showed that the quaternary structure is predominantly dimeric in solution, and only minor conformational and dynamical changes occur in the DNA bound state. Also, labeling of the unresolved C- terminus revealed no significant change in dynamics upon DNA binding. These observations are unique, since for other bacterial copper metalloregulators, such as the MerR and CopY families, major conformational changes were observed upon DNA binding, indicating a different mode of action for this protein family.

Loop Evolutionary Patterns Shape Catalytic Efficiency of TRI101/201 for Trichothecenes: Insights into Protein-Substrate Interactions

Trichothecenes are highly toxic mycotoxins produced by Fusarium fungi, while TRI101/201 family enzymes play a crucial role in detoxification through acetylation. Studies on the substrate specificity and catalytic kinetics of TRI101/201 have revealed distinct kinetic characteristics, with significant differences observed in catalytic efficiency toward deoxynivalenol, while the catalytic efficiency for T-2 toxin remains relatively consistent. In this study, we used structural bioinformatics analysis and a molecular dynamics simulation workflow to investigate the mechanism underlying the differential catalytic activity of TRI101/201. The findings revealed that the binding stability between trichothecenes and TRI101/201 hinges primarily on a hydrophobic cage structure within the binding site. An intrinsic disordered loop, termed loop cover, defined the evolutionary patterns of the TRI101/201 protein family that are categorized into four subfamilies (V1/V2/V3/M). Furthermore, the unique loop displayed different conformations among these subfamilies' structures, which served to disrupt (V1/V2/V3) or reinforce (M) the hydrophobic cages. The disrupted cages enhanced the water exposure of the hydrophilic moieties of substrates like deoxynivalenol and thereby hindered their binding to the catalytic sites of V-type enzymes. In contrast, this water exposure does not affect substrates like T-2 toxin, which have more hydrophobic substituents, resulting in a comparable catalytic efficiency of both V- and M-type enzymes. Overall, our studies provide theoretical support for understanding the catalytic mechanism of TRI101/201, which shows how an intrinsic disordered loop could impact the protein-ligand binding and suggests a direction for rational protein design in the future.

EquiPNAS: improved protein-nucleic acid binding site prediction using protein-language-model-informed equivariant deep graph neural networks

Protein language models (pLMs) trained on a large corpus of protein sequences have shown unprecedented scalability and broad generalizability in a wide range of predictive modeling tasks, but their power has not yet been harnessed for predicting protein-nucleic acid binding sites, critical for characterizing the interactions between proteins and nucleic acids. Here we present EquiPNAS, a new pLM-informed E(3) equivariant deep graph neural network framework for improved protein-nucleic acid binding site prediction. By combining the strengths of pLM and symmetry-aware deep graph learning, EquiPNAS consistently outperforms the state-of-the-art methods for both protein-DNA and protein-RNA binding site prediction on multiple datasets across a diverse set of predictive modeling scenarios ranging from using experimental input to AlphaFold2 predictions. Our ablation study reveals that the pLM embeddings used in EquiPNAS are sufficiently powerful to dramatically reduce the dependence on the availability of evolutionary information without compromising on accuracy, and that the symmetry-aware nature of the E(3) equivariant graph-based neural architecture offers remarkable robustness and performance resilience. EquiPNAS is freely available at https://github.com/Bhattacharya-Lab/EquiPNAS.

In Silico Investigation of AKT2 Gene and Protein Abnormalities Reveals Potential Association with Insulin Resistance and Type 2 Diabetes

Type 2 diabetes (T2D) develops from insulin resistance (IR) and the dysfunction of pancreatic beta cells. The AKT2 protein is very important for the protein signaling pathway, and the non-synonymous SNP (nsSNPs) in AKT2 gene may be associated with T2D. nsSNPs can result in alterations in protein stability, enzymatic activity, or binding specificity. The objective of this study was to investigate the effect of nsSNPs on the AKT2 protein structure and function that may result in the induction of IR and T2D. The study identified 20 variants that were considered to be the most deleterious based on a range of analytical tools included (SIFT, PolyPhen2, Mut-pred, SNAP2, PANTHER, PhD-SNP, SNP&Go, MUpro, Cosurf, and I-Mut). Two mutations, p.A179T and p.L183Q, were selected for further investigation based on their location within the protein as determined by PyMol. The results indicated that mutations, p.A179T and p.L183Q alter the protein stability and functional characteristics, which could potentially affect its function. In order to conduct a more in-depth analysis of these effects, a molecular dynamics simulation was performed for wildtype AKT2 and the two mutants (p.A179T and p.L183Q). The simulation evaluated various parameters, including temperature, pressure, density, RMSD, RMSF, SASA, and Region, over a period of 100 ps. According to the simulation results, the wildtype AKT2 protein demonstrated higher stability in comparison to the mutant variants. The mutations p.A179T and p.L183Q were found to cause a reduction in both protein stability and functionality. These findings underscore the significance of the effects of nsSNPs (mutations p.A179T and p.L183Q) on the structure and function of AKT2 that may lead to IR and T2D. Nevertheless, they require further verifications in future protein functional, protein-protein interaction, and large-scale case-control studies. When verified, these results will help in the identification and stratification of individuals who are at risk of IR and T2D for the purpose of prevention and treatment.

Comprehensive analysis predicting effects of deleterious SNPs of human progesterone receptor gene on its structure and functions: a computational approach

Progesterone receptor plays a crucial role in the development of the mammary gland and breast cancer. Single nucleotide polymorphisms (SNPs) within its gene, PGR, are associated with the risk of miscarriages and preterm birth as well as many cancers across different populations. The main aim of this work is to investigate the most deleterious SNPs in the PGR gene to identify potential biomarkers for various disease susceptibility and treatments. Both sequence and structure-based computational approaches were adopted and in total 11 nsSNPs have been filtered out of 674 nsSNPs along with seven non-coding SNPs. R740Q, I744T and D746E belonged to a mutation cluster. R740Q, D746E along with S865L altered H-bond interactions within the receptor. The same mutations have been found to be associated with several cancers including uterine and breast cancer among others. It is, therefore, possible that the high-risk SNPs associated with cancers may exert their effect by causing changes in the protein structure, particularly in its bonding patterns, and thus affecting its function. In addition, seven non-coding SNPs that were located in the UTR region created a new miRNA site while three SNPs disrupted a conserved miRNA site. These high-risk SNPs can play an instrumental role in generating a dataset of the PGR gene's SNPs. Thus, the present study may pave the way to design and develop novel therapeutics for overcoming the challenges associated with certain cancers and pregnancy that result from a change in the protein structure and function due to the SNP mutations in the PGR gene.Communicated by Ramaswamy H. Sarma.

Deciphering the enigma of RNA editing in the ATP1_alpha subunit of ATP synthase in Triticum aestivum

There is evidence that RNA editing is related to plant cellular stress as well as electron transport organelles, such as mitochondria. The mitochondrial atp1 gene encodes the alpha-subunit of Atp synthase. Control as well as two periods of drought stress treatments were analyzed in the cDNAs generated from the mitochondrial atp1 gene of two cultivars of Triticum aestivum [Giza 168 (G168) and Gemmiza 10 (GM10)]. Following RNA-seq data assembly, atp1 cDNAs from the control (acc. no. OQ129415), 2-hour (acc. no. OQ129416), and 12-hour (acc. no. OQ129417) time points of the T. aestivum cultivar G168 were obtained. Control (acc. no. OQ129419), 2-hour (acc. no. OQ129420), and 12-hour (acc. no. OQ129421) samples all included reconstructed atp1 transcripts from Gemmiza 10. Atp1 transcripts were assembled using the wheat atp1 gene (acc. no. NC_036024). RNA-seq raw data was utilized to identify 11 RNA editing sites in atp1 in the tolerant cultivar Giza168 and 6 in the sensitive cultivar Gemmiza10. The significant difference in RNA editing observed between control and drought stress conditions in sites led to synonymous amino acids. This led to no change in tertiary structure between tolerant and sensitive cultivars. But the change was focused between produced protein and its correspondence sequence on DNA.

Molecular Mimicry Mapping in Streptococcus pneumoniae: Cues for Autoimmune Disorders and Implications for Immune Defense Activation

Streptococcus pneumoniae contributes to a range of infections, including meningitis, pneumonia, otitis media, and sepsis. Infections by this bacterium have been associated with the phenomenon of molecular mimicry, which, in turn, may contribute to the induction of autoimmunity. In this study, we utilized a bioinformatics approach to investigate the potential for S. pneumoniae to incite autoimmunity via molecular mimicry. We identified 13 S. pneumoniae proteins that have significant sequence similarity to human proteins, with 11 of them linked to autoimmune disorders such as psoriasis, rheumatoid arthritis, and diabetes. Using in silico tools, we predicted the sequence as well as the structural homology among these proteins. Database mining was conducted to establish links between these proteins and autoimmune disorders. The antigenic, non-allergenic, and immunogenic sequence mimics were employed to design and validate an immune response via vaccine construct design. Mimic-based vaccine construct can prove effective for immunization against the S. pneumoniae infections. Immune response simulation and binding affinity was assessed through the docking of construct C8 to human leukocyte antigen (HLA) molecules and TLR4 receptor, with promising results. Additionally, these mimics were mapped as conserved regions on their respective proteins, suggesting their functional importance in S. pneumoniae pathogenesis. This study highlights the potential for S. pneumoniae to trigger autoimmunity via molecular mimicry and the possibility of vaccine design using these mimics for triggering defense response.

Structural features of the Notch ankyrin domain-Deltex WWE2 domain heterodimer determined by NMR spectroscopy and functional implications

The Notch signaling pathway, an important cell fate determination pathway, is modulated by the ubiquitin ligase Deltex. Here, we investigate the structural basis for Deltex-Notch interaction. We used nuclear magnetic resonance (NMR) spectroscopy to assign the backbone of the Drosophila Deltex WWE2 domain and mapped the binding site of the Notch ankyrin (ANK) domain to the N-terminal WWEA motif. Using cultured Drosophila S2R+ cells, we find that point substitutions within the ANK-binding surface of Deltex disrupt Deltex-mediated enhancement of Notch transcriptional activation and disrupt ANK binding in cells and in vitro. Likewise, ANK substitutions that disrupt Notch-Deltex heterodimer formation in vitro block disrupt Deltex-mediated stimulation of Notch transcription activation and diminish interaction with full-length Deltex in cells. Surprisingly, the Deltex-Notch intracellular domain (NICD) interaction is not disrupted by deletion of the Deltex WWE2 domain, suggesting a secondary Notch-Deltex interaction. These results show the importance of the WWEA:ANK interaction in enhancing Notch signaling.

Unique dimeric structure of the DUF2891 family protein CJ0554 from Campylobacter jejuni

Campylobacter jejuni is a pathogenic bacterium that causes enteritis and Guillain-Barre syndrome in humans. To identify a protein target for the development of a new therapeutic against C. jejuni infection, each gene product of C. jejuni must be functionally characterized. The cj0554 gene of C. jejuni encodes a DUF2891 family protein with unknown functions. To provide functional insights into CJ0554, we determined and analyzed the crystal structure of the CJ0554 protein. CJ0554 adopts an (α/α)₆-barrel structure, which consists of an inner α₆ ring and an outer α₆ ring. CJ0554 assembles into a dimer in a unique top-to-top orientation that is not observed in its structural homologs, N-acetylglucosamine 2-epimerase superfamily members. Dimer formation was verified by analyzing CJ0554 and its ortholog protein through gel-filtration chromatography. The top of the CJ0554 monomer barrel harbors a cavity, which is connected to that of the second subunit in the dimer structure, generating a larger intersubunit cavity. This elongated cavity accommodates extra nonproteinaceous electron density, presumably as a pseudosubstrate, and is lined with generally catalytically active histidine residues that are invariant in CJ0554 orthologs. Therefore, we propose that the cavity functions as the active site of CJ0554.

Using evolutionary data to make sense of macromolecules with a "face-lifted" ConSurf

The ConSurf web-sever for the analysis of proteins, RNA, and DNA provides a quick and accurate estimate of the per-site evolutionary rate among homologues. The analysis reveals functionally important regions, such as catalytic and ligand-binding sites, which often evolve slowly. Since the last report in 2016, ConSurf has been improved in multiple ways. It now has a user-friendly interface that makes it easier to perform the analysis and to visualize the results. Evolutionary rates are calculated based on a set of homologous sequences, collected using hidden Markov model-based search tools, recently embedded in the pipeline. Using these, and following the removal of redundancy, ConSurf assembles a representative set of effective homologues for protein and nucleic acid queries to enable informative analysis of the evolutionary patterns. The analysis is particularly insightful when the evolutionary rates are mapped on the macromolecule structure. In this respect, the availability of AlphaFold model structures of essentially all UniProt proteins makes ConSurf particularly relevant to the research community. The UniProt ID of a query protein with an available AlphaFold model can now be used to start a calculation. Another important improvement is the Python re-implementation of the entire computational pipeline, making it easier to maintain. This Python pipeline is now available for download as a standalone version. We demonstrate some of ConSurf's key capabilities by the analysis of caveolin-1, the main protein of membrane invaginations called caveolae.

The Drug-Induced Interface That Drives HIV-1 Integrase Hypermultimerization and Loss of Function

Allosteric HIV-1 integrase (IN) inhibitors (ALLINIs) are an emerging class of small molecules that disrupt viral maturation by inducing the aberrant multimerization of IN. Here, we present cocrystal structures of HIV-1 IN with two potent ALLINIs, namely, BI-D and the drug candidate Pirmitegravir. The structures reveal atomistic details of the ALLINI-induced interface between the HIV-1 IN catalytic core and carboxyl-terminal domains (CCD and CTD). Projecting from their principal binding pocket on the IN CCD dimer, the compounds act as molecular glue by engaging a triad of invariant HIV-1 IN CTD residues, namely, Tyr226, Trp235, and Lys266, to nucleate the CTD-CCD interaction. The drug-induced interface involves the CTD SH3-like fold and extends to the beginning of the IN carboxyl-terminal tail region. We show that mutations of HIV-1 IN CTD residues that participate in the interface with the CCD greatly reduce the IN-aggregation properties of Pirmitegravir. Our results explain the mechanism of the ALLINI-induced condensation of HIV-1 IN and provide a reliable template for the rational development of this series of antiretrovirals through the optimization of their key contacts with the viral target. IMPORTANCE Despite the remarkable success of combination antiretroviral therapy, HIV-1 remains among the major causes of human suffering and loss of life in poor and developing nations. To prevail in this drawn-out battle with the pandemic, it is essential to continue developing advanced antiviral agents to fight drug resistant HIV-1 variants. Allosteric integrase inhibitors (ALLINIs) are an emerging class of HIV-1 antagonists that are orthogonal to the current antiretroviral drugs. These small molecules act as highly specific molecular glue, which triggers the aggregation of HIV-1 integrase. In this work, we present high-resolution crystal structures that reveal the crucial interactions made by two potent ALLINIs, namely, BI-D and Pirmitegravir, with HIV-1 integrase. Our results explain the mechanism of drug action and will inform the development of this promising class of small molecules for future use in antiretroviral regimens.

Application of Molecular Simulation Methods in Food Science: Status and Prospects

Molecular simulation methods, such as molecular docking, molecular dynamic (MD) simulation, and quantum chemical (QC) calculation, have become popular as characterization and/or virtual screening tools because they can visually display interaction details that in vitro experiments can not capture and quickly screen bioactive compounds from large databases with millions of molecules. Currently, interdisciplinary research has expanded molecular simulation technology from computer aided drug design (CADD) to food science. More food scientists are supporting their hypotheses/results with this technology. To understand better the use of molecular simulation methods, it is necessary to systematically summarize the latest applications and usage trends of molecular simulation methods in the research field of food science. However, this type of review article is rare. To bridge this gap, we have comprehensively summarized the principle, combination usage, and application of molecular simulation methods in food science. We also analyzed the limitations and future trends and offered valuable strategies with the latest technologies to help food scientists use molecular simulation methods.

Structure and IgE Cross-Reactivity among Cashew, Pistachio, Walnut, and Peanut Vicilin-Buried Peptides

Peanut and tree-nut allergies are frequently comorbid for reasons not completely understood. Vicilin-buried peptides (VBPs) are an emerging family of food allergens whose conserved structural fold could mediate peanut/tree-nut co-allergy. Peptide microarrays were used to identify immunoglobulin E (IgE) epitopes from the N-terminus of the vicilin allergens Ara h 1, Ana o 1, Jug r 2, and Pis v 3 using serum from three patient diagnosis groups: monoallergic to either peanuts or cashew/pistachio, or dual allergic. IgE binding peptides were highly prevalent in the VBP domains AH1.1, AO1.1, JR2.1, and PV3.1, but not in AO1.2, JR2.2, JR2.3, and PV3.2 nor the unstructured regions. The IgE profiles did not correlate with diagnosis group. The structure of the VBPs from cashew and pistachio was solved using solution-NMR. Comparisons of structural features suggest that the VBP scaffold from peanuts and tree-nuts can support cross-reactivity. This may help understand comorbidity and cross-reactivity despite a distant evolutionary origin.

Structural Model of a Porphyromonas gingivalis type IX Secretion System Shuttle Complex

Porphyromonas gingivalis is a gram-negative oral anaerobic pathogen and is one of the key causative agents of periodontitis. P. gingivalis utilises a range of virulence factors, including the cysteine protease RgpB, to drive pathogenesis and these are exported and attached to the cell surface via the type IX secretion system (T9SS). All cargo proteins possess a conserved C-terminal signal domain (CTD) which is recognised by the T9SS, and the outer membrane β-barrel protein PorV (PG0027/LptO) can interact with cargo proteins as they are exported to the bacterial surface. Using a combination of solution nuclear magnetic resonance (NMR) spectroscopy, biochemical analyses, machine-learning-based modelling and molecular dynamics (MD) simulations, we present a structural model of a PorV:RgpB-CTD complex from P. gingivalis. This is the first structural insight into CTD recognition by the T9SS and shows how the conserved motifs in the CTD are the primary sites that mediate binding. In PorV, interactions with extracellular surface loops are important for binding the CTD, and together these appear to cradle and lock RgpB-CTD in place. This work provides insight into cargo recognition by PorV but may also have important implications for understanding other aspects of type-IX dependent secretion.

Analysis of single-nucleotide polymorphisms in genes associated with triple-negative breast cancer

Triple-negative breast cancer (TNBC) is a rare variant of breast cancer (BC) known to be aggressive and refractory. TNBC lacks effective early diagnostic and therapeutic options leading to poorer outcomes. The genomic landscape and alterations leading to BC and TNBC are vast and unclear. Single nucleotide polymorphisms (SNPs) are a widespread form of genetic alterations with a multi-faceted impact on multiple diseases, including BC and TNBC. In this study, we attempted to construct a framework that could identify genes associated with TNBC and screen the SNPs reported in these genes using a set of computational predictors. This framework helped identify BRCA1, BRCA2, EGFR, PIK3CA, PTEN, and TP53 as recurrent genes associated with TNBC. We found 2%-29% of reported SNPs across genes to be typed pathogenic by all the predictors in the framework. We demonstrate that our framework prediction on BC samples identifies 99% of alterations as pathogenic by at least one predictor and 32% as pathogenic by all the predictors. Our framework could be an initial step in developing an early diagnosis of TNBC and potentially help improve the understanding of therapeutic resistance and sensitivity.

Classification of Protein-Binding Sites Using a Spherical Convolutional Neural Network

The analysis and comparison of protein-binding sites aid various applications in the drug discovery process, e.g., hit finding, drug repurposing, and polypharmacology. Classification of binding sites has been a hot topic for the past 30 years, and many different methods have been published. The rapid development of machine learning computational algorithms, coupled with the large volume of publicly available protein-ligand 3D structures, makes it possible to apply deep learning techniques in binding site comparison. Our method uses a cutting-edge spherical convolutional neural network based on the DeepSphere architecture to learn global representations of protein-binding sites. The model was trained on TOUGH-C1 and TOUGH-M1 data and validated with the ProSPECCTs datasets. Our results show that our model can (1) perform well in protein-binding site similarity and classification tasks and (2) learn and separate the physicochemical properties of binding sites. Lastly, we tested the model on a set of kinases, where the results show that it is able to cluster the different kinase subfamilies effectively. This example demonstrates the method's promise for lead hopping within or outside a protein target, directly based on binding site information.

Intrinsic protein disorder uncouples affinity from binding specificity

Intrinsically disordered proteins (IDPs) and intrinsically disordered regions (IDRs) of proteins often function by molecular recognition, in which they undergo induced folding. Based on prior generalizations, the idea prevails in the IDP field that due to the entropic penalty of induced folding, the major functional advantage associated with this binding mode is "uncoupling" specificity from binding strength. Nevertheless, both weaker binding and high specificity of IDPs/IDRs rest on limited experimental observations, making these assumptions more speculations than evidence-supported facts. The issue is also complicated by the rather vague concept of specificity that lacks an exact measure, such as the Kd for binding strength. We addressed these issues by creating and analyzing a comprehensive dataset of well-characterized ID/globular protein complexes, for which both the atomic structure of the complex and free energy (ΔG, Kd ) of interaction is known. Through this analysis, we provide evidence that the affinity distributions of IDP/globular and globular/globular complexes show different trends, whereas specificity does not connote to weaker binding strength of IDPs/IDRs. Furthermore, protein disorder extends the spectrum in the direction of very weak interactions, which may have important regulatory consequences and suggest that, in a biological sense, strict correlation of specificity and binding strength are uncoupled by structural disorder.

Genome interpretation using in silico predictors of variant impact

Estimating the effects of variants found in disease driver genes opens the door to personalized therapeutic opportunities. Clinical associations and laboratory experiments can only characterize a tiny fraction of all the available variants, leaving the majority as variants of unknown significance (VUS). In silico methods bridge this gap by providing instant estimates on a large scale, most often based on the numerous genetic differences between species. Despite concerns that these methods may lack reliability in individual subjects, their numerous practical applications over cohorts suggest they are already helpful and have a role to play in genome interpretation when used at the proper scale and context. In this review, we aim to gain insights into the training and validation of these variant effect predicting methods and illustrate representative types of experimental and clinical applications. Objective performance assessments using various datasets that are not yet published indicate the strengths and limitations of each method. These show that cautious use of in silico variant impact predictors is essential for addressing genome interpretation challenges.

Identification and Mutation Analysis of Nonconserved Residues on the TIM-Barrel Surface of GH5_5 Cellulases for Catalytic Efficiency and Stability Improvement

Exploring the potential functions of nonconserved residues on the outer side of α-helices and systematically optimizing them are pivotal for their application in protein engineering. Based on the evolutionary structural conservation analysis of GH5_5 cellulases, a practical molecular improvement strategy was developed. Highly variable sites on the outer side of the α-helices of the GH5_5 cellulase from Aspergillus niger (AnCel5A) were screened, and 14 out of the 34 highly variable sites were confirmed to exert a positive effect on the activity. After the modular combination of the positive mutations, the catalytic efficiency of the mutants was further improved. By using CMC-Na as the substrate, the catalytic efficiency and specific activity of variant AnCel5A_N193A/T300P/D307P were approximately 2.0-fold that of AnCel5A (227 ± 21 versus 451 ± 43 ml/s/mg and 1,726 ± 19 versus 3,472 ± 42 U/mg, respectively). The half-life (t_1/2) of variant AnCel5A_N193A/T300P/D307P at 75°C was 2.36 times that of AnCel5A. The role of these sites was successfully validated in other GH5_5 cellulases. Computational analyses revealed that the flexibility of the loop 6-loop 7-loop 8 region was responsible for the increased catalytic performance. This work not only illustrated the important role of rapidly evolving positions on the outer side of the α-helices of GH5_5 cellulases but also revealed new insights into engineering the proteins that nature left as clues for us to find. IMPORTANCE A comprehensive understanding of the residues on the α-helices of the GH5_5 cellulases is important for catalytic efficiency and stability improvement. The main objective of this study was to use the evolutionary conservation and plasticity of the TIM-barrel fold to probe the relationship between nonconserved residues on the outer side of the α-helices and the catalytic efficiency of GH5_5 cellulases by conducting structure-guided protein engineering. By using a four-step nonconserved residue screening strategy, the functional role of nonconserved residues on the outer side of the α-helices was effectively identified, and a variant with superior performance and capability was constructed. Hence, this study proved the effectiveness of this strategy in engineering GH5_5 cellulases and provided a potential competitor for industrial applications. Furthermore, this study sheds new light on engineering TIM-barrel proteins.

In silico comprehensive analysis of coding and non-coding SNPs in human mTOR protein

The mammalian/mechanistic target of rapamycin (mTOR) protein is an important growth regulator and has been linked with multiple diseases including cancer and diabetes. Non-synonymous mutations of this gene have already been found in patients with renal clear cell carcinoma, melanoma, and acute lymphoid leukemia among many others. Such mutations can potentially affect a protein’s structure and hence its functions. In this study, therefore, the most deleterious SNPs of mTOR protein have been determined to identify potential biomarkers for various disease treatments. The aim is to generate a structured dataset of the mTOR gene’s SNPs that may prove to be an asset for the identification and treatment of multiple diseases associated with the target gene. Both sequence and structure-based approaches were adopted and a wide variety of bioinformatics tools were applied to analyze the SNPs of mTOR protein. In total 11 nsSNPs have been filtered out of 2178 nsSNPs along with two non-coding variations. All of the nsSNPs were found to destabilize the protein structure and disrupt its function. While R619C, A1513D, and T1977R mutations were shown to alter C alpha distances and bond angles of the mTOR protein, L509Q, R619C and N2043S were predicted to disrupt the mTOR protein’s interaction with NBS1 protein and FKBP1A/rapamycin complex. In addition, one of the non-coding SNPs was shown to alter miRNA binding sites. Characterizing nsSNPs and non-coding SNPs and their harmful effects on a protein’s structure and functions will enable researchers to understand the critical impact of mutations on the molecular mechanisms of various diseases. This will ultimately lead to the identification of potential targets for disease diagnosis and therapeutic interventions.

Computational Insights into the Structural and Functional Impacts of nsSNPs of Bone Morphogenetic Proteins

BMPs (bone morphogenetic proteins) are multipurpose (transforming growth factor)TGF-superfamily released cytokines. These glycoproteins, acting as disulfide-linked homo- or heterodimers, are highly potent regulators of bone and cartilage production and repair, cell proliferation throughout embryonic development, and bone homeostasis in the adults. Due to the fact that genetic variation might influence structural functions, this study is aimed to determine the pathogenic effect of nonsynonymous single-nucleotide polymorphisms (nsSNPs) in BMP genes. The implications of these variations, investigated using computational analysis and molecular models of the mature TGF-β domain, revealed the impact of modifications on the function of BMP protein. The three-dimensional (3D) structure analysis was performed on the nsSNP Y316S, V386G, E387G, C389G, and C391G nsSNP in the TGF-β domain of chicken BMP2 and H344P, S347P, V357A nsSNP in the TGF-β domain of chicken BMP4 protein that was anticipated to be harmful and of high risk. The ability of the proteins to perform variety of tasks interact with other molecules depends on their tertiary structural composition. The current analysis revealed the four most damaging variants (Y316S, V386G, E387G, C389G, and C391G), highly conserved and functional and are located in the TGF-beta domain of BMP2 and BMP4. The amino acid substitutions E387G, C389G, and C391G are discovered in the binding region. It was observed that the mutations in the TGF-beta domain caused significant changes in its structural organization including the substrate binding sites. Current findings will assist future research focused on the role of these variants in BMP function loss and their role in skeletal disorders, and this will possibly help to develop practical strategies for treating bone-related conditions.

Structure, receptor recognition, and antigenicity of the human coronavirus CCoV-HuPn-2018 spike glycoprotein

The isolation of CCoV-HuPn-2018 from a child respiratory swab indicates that more coronaviruses are spilling over to humans than previously appreciated. We determined the structures of the CCoV-HuPn-2018 spike glycoprotein trimer in two distinct conformational states and showed that its domain 0 recognizes sialosides. We identified that the CCoV-HuPn-2018 spike binds canine, feline, and porcine aminopeptidase N (APN) orthologs, which serve as entry receptors, and determined the structure of the receptor-binding B domain in complex with canine APN. The introduction of an oligosaccharide at position N739 of human APN renders cells susceptible to CCoV-HuPn-2018 spike-mediated entry, suggesting that single-nucleotide polymorphisms might account for viral detection in some individuals. Human polyclonal plasma antibodies elicited by HCoV-229E infection and a porcine coronavirus monoclonal antibody inhibit CCoV-HuPn-2018 spike-mediated entry, underscoring the cross-neutralizing activity among ɑ-coronaviruses. These data pave the way for vaccine and therapeutic development targeting this zoonotic pathogen representing the eighth human-infecting coronavirus.