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At least 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

DeepComplex: A Web Server of Predicting Protein Complex Structures by Deep Learning Inter-chain Contact Prediction and Distance-Based Modelling

Proteins interact to form complexes. Predicting the quaternary structure of protein complexes is useful for protein function analysis, protein engineering, and drug design. However, few user-friendly tools leveraging the latest deep learning technology for inter-chain contact prediction and the distance-based modelling to predict protein quaternary structures are available. To address this gap, we develop DeepComplex, a web server for predicting structures of dimeric protein complexes. It uses deep learning to predict inter-chain contacts in a homodimer or heterodimer. The predicted contacts are then used to construct a quaternary structure of the dimer by the distance-based modelling, which can be interactively viewed and analysed. The web server is freely accessible and requires no registration. It can be easily used by providing a job name and an email address along with the tertiary structure for one chain of a homodimer or two chains of a heterodimer. The output webpage provides the multiple sequence alignment, predicted inter-chain residue-residue contact map, and predicted quaternary structure of the dimer.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

A large accessory protein interactome is rewired across environments

To characterize how protein-protein interaction (PPI) networks change, we quantified the relative PPI abundance of 1.6 million protein pairs in the yeast Saccharomyces cerevisiae across 9 growth conditions, with replication, for a total of 44 million measurements. Our multi-condition screen identified 13,764 pairwise PPIs, a 3-fold increase over PPIs identified in one condition. A few 'immutable' PPIs are present across all conditions, while most 'mutable' PPIs are rarely observed. Immutable PPIs aggregate into highly connected 'core' network modules, with most network remodeling occurring within a loosely connected 'accessory' module. Mutable PPIs are less likely to co-express, co-localize, and be explained by simple mass action kinetics, and more likely to contain proteins with intrinsically disordered regions, implying that environment-dependent association and binding is critical to cellular adaptation. Our results show that protein interactomes are larger than previously thought and contain highly dynamic regions that reorganize to drive or respond to cellular changes.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Genome-wide approaches delineate the additive, epistatic, and pleiotropic nature of variants controlling fatty acid composition in peanut ( Arachis hypogaea L.)

Abstract The fatty acid composition of seed oil is a major determinant of the flavor, shelf-life, and nutritional quality of peanuts. Major QTLs controlling high oil content, high oleic content, and low linoleic content have been characterized in several seed oil crop species. Here, we employ genome-wide association approaches on a recently genotyped collection of 787 plant introduction accessions in the USDA peanut core collection, plus selected improved cultivars, to discover markers associated with the natural variation in fatty acid composition, and to explain the genetic control of fatty acid composition in seed oils. Overall, 251 single nucleotide polymorphisms (SNPs) had significant trait associations with the measured fatty acid components. Twelve SNPs were associated with two or three different traits. Of these loci with apparent pleiotropic effects, 10 were associated with both oleic (C18:1) and linoleic acid (C18:2) content at different positions in the genome. In all 10 cases, the favorable allele had an opposite effect—increasing and lowering the concentration, respectively, of oleic and linoleic acid. The other traits with pleiotropic variant control were palmitic (C16:0), behenic (C22:0), lignoceric (C24:0), gadoleic (C20:1), total saturated, and total unsaturated fatty acid content. One hundred (100) of the significantly associated SNPs were located within 1000 kbp of 55 genes with fatty acid biosynthesis functional annotations. These genes encoded, among others: ACCase carboxyl transferase subunits, and several fatty acid synthase II enzymes. With the exception of gadoleic (C20:1) and lignoceric (C24:0) acid content, which occur at relatively low abundance in cultivated peanuts, all traits had significant SNP interactions exceeding a stringent Bonferroni threshold (α = 1%). We detected 7682 pairwise SNP interactions affecting the relative abundance of fatty acid components in the seed oil. Of these, 627 SNP pairs had at least one SNP within 1000 kbp of a gene with fatty acid biosynthesis functional annotation. We evaluated 168 candidate genes underlying these SNP interactions. Functional enrichment and protein-to-protein interactions supported significant interactions (P-value < 1.0E−16) among the genes evaluated. These results show the complex nature of the biology and genes underlying the variation in seed oil fatty acid composition and contribute to an improved genotype-to-phenotype map for fatty acid variation in peanut seed oil.

Otyama, Paul I.↗
DOE OSTI2023

Structural basis of the amidase ClbL central to the biosynthesis of the genotoxin colibactin

Colibactin is a genotoxic natural product produced by select commensal bacteria in the human gut microbiota. The compound is a bis-electrophile that is predicted to form interstrand DNA cross-links in target cells, leading to double-strand DNA breaks. The biosynthesis of colibactin is carried out by a mixed NRPS–PKS assembly line with several noncanonical features. An amidase, ClbL, plays a key role in the pathway, catalyzing the final step in the formation of the pseudodimeric scaffold. ClbL couples α-aminoketone and β-ketothioester intermediates attached to separate carrier domains on the NRPS–PKS assembly. Here, the 1.9 Å resolution structure of ClbL is reported, providing a structural basis for this key step in the colibactin biosynthetic pathway. The structure reveals an open hydrophobic active site surrounded by flexible loops, and comparison with homologous amidases supports its unusual function and predicts macromolecular interactions with pathway carrier-protein substrates. Modeling protein–protein interactions supports a predicted molecular basis for enzyme–carrier domain interactions. Overall, the work provides structural insight into this unique enzyme that is central to the biosynthesis of colibactin.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

A selective WDR5 degrader inhibits acute myeloid leukemia in patient-derived mouse models

Interactions between WD40 repeat domain protein 5 (WDR5) and its various partners such as mixed lineage leukemia (MLL) and c-MYC are essential for sustaining oncogenesis in human cancers. However, inhibitors that block protein-protein interactions (PPIs) between WDR5 and its binding partners exhibit modest cancer cell killing effects and lack in vivo efficacy. Here, we present pharmacological degradation of WDR5 as a promising therapeutic strategy for treating WDR5-dependent tumors and report two high-resolution crystal structures of WDR5-degrader-E3 ligase ternary complexes. We identified an effective WDR5 degrader via structure-based design and demonstrated its in vitro and in vivo antitumor activities. On the basis of the crystal structure of an initial WDR5 degrader in complex with WDR5 and the E3 ligase von Hippel–Lindau (VHL), we designed a WDR5 degrader, MS67, and demonstrated the high cooperativity of MS67 binding to WDR5 and VHL by another ternary complex structure and biophysical characterization. MS67 potently and selectively depleted WDR5 and was more effective than WDR5 PPI inhibitors in suppressing transcription of WDR5-regulated genes, decreasing the chromatin-bound fraction of MLL complex components and c-MYC, and inhibiting the proliferation of cancer cells. In addition, MS67 suppressed malignant growth of MLL-rearranged acute myeloid leukemia patient cells in vitro and in vivo and was well tolerated in vivo. Collectively, our results demonstrate that structure-based design can be an effective strategy to identify highly active degraders and suggest that pharmacological degradation of WDR5 might be a promising treatment for WDR5-dependent cancers.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2025

Plant Defense Proteins: Recent Discoveries and Applications

Proteins play pivotal roles in safeguarding plants against numerous biotic and abiotic stresses. Understanding their biological functions and mechanisms of action is essential for advancing plant biology, agriculture, and biotechnology. This review considers the diversity and potential applications of plant defense proteins including pathogenesis-related (PR) proteins, chitinases, glucanases, protease inhibitors, lectins, and antimicrobial peptides. Recent advances, such as the omics technologies, have enabled the discovery of new plant defense proteins and regulatory networks that govern plant defense responses and unveiled numerous roles of plant defense proteins in stress perception, signal transduction, and immune priming. The molecular affinities and enzymatic activities of plant defense proteins are essential for their defense functions. Applications of plant defense proteins span agriculture, biotechnology, and medicine, including the development of resistant crop varieties, bio-based products, biopharmaceuticals, and functional foods. Future research directions include elucidating the structural bases of defense protein functions, exploring protein interactions with ligands and other proteins, and engineering defense proteins for enhanced efficacy. Overall, this review illuminates the significance of plant defense proteins against biotic stresses in plant biology and biotechnology, emphasizing their potential for sustainable agriculture and environmental management.

G-proteins↗
DOE OSTI2020

SERTA Domain Containing Protein 1 (SERTAD1) Interacts with Classical Swine Fever Virus Structural Glycoprotein E2, Which Is Involved in Virus Virulence in Swine

E2 is the major structural glycoprotein of the classical swine fever virus (CSFV). E2 has been shown to be involved in important virus functions such as replication and virulence in swine. Using the yeast two-hybrid system, we previously identified several host proteins specifically interacting with CSFV E2. Here, we analyze the protein interaction of E2 with SERTA domain containing protein 1 (SERTAD1), a factor involved in the stimulation of the transcriptional activities of different host genes. We have confirmed that the interaction between these two proteins occurs in CSFV-infected swine cells by using a proximity ligation assay and confocal microscopy. Amino acid residues in the CSFV E2 protein that are responsible for mediating the interaction with SERTAD1 were mapped by a yeast two-hybrid approach using a randomly mutated E2 library. Using that information, a recombinant CSFV mutant (E2ΔSERTAD1v) that harbors substitutions in those residues mediating the protein-interaction with SERTAD1 was developed and used to study the role of the E2-SERTAD1 interaction in viral replication and virulence in swine. CSFV E2ΔSERTAD1v, when compared to the parental BICv, showed a clearly decreased ability to replicate in the SK6 swine cell line and a more severe replication defect in primary swine macrophage cultures. Importantly, 80% of animals infected with E2ΔSERTAD1v survived infection, remaining clinically normal during the 21-day observational period. This result would indicate that the ability of CSFV E2 to bind host SERTAD1 protein during infection plays a critical role in virus virulence.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

LxCxD motif of the APC/C coactivator subunit FZR1 is critical for interaction with the retinoblastoma protein

Highlights: • Several oncogenic viral proteins bind to tumor suppressor pRB via LxCxE motif. • LxCxE present in many cellular proteins that interact with pRB. • APC/C interacts with pRB via this motif in its FZR1 subunit. • FZR1 LxCxE-like LxCxD critical for FZR1-pRB direct binding and SKP2 ubiquitylation. • Mutation of FZR1 LxCxD alters cell cycle. Retinoblastoma protein (pRB) regulates cell cycle by utilizing different regions of its pocket domain for interacting with E2F family of transcription factors and with cellular and viral proteins containing an LxCxE motif. An LxCxE-like motif, LxCxD, is present in FZR1, an adaptor protein of the multi-subunit E3 ligase complex anaphase-promoting complex/cyclosome (APC/C). The APC/C{sup FZR1} complex regulates the timely degradation of multiple cell cycle proteins for mitotic exit and maintains G1 state. We report that FZR1 interacts with pRB via its LxCxD motif. By using point mutations, we found that the cysteine residue in the FZR1 LxCxD motif is critical for direct interaction with pRb. The direct binding of the LxCxD motif of FZR1 to the pRB LxCxE binding pocket is confirmed by using human papillomavirus protein E7 as a competitor, both in vitro and in vivo. While mutation of the cysteine residue significantly disrupts FZR1 interaction with pRB, this motif does not affect FZR1 and core APC/C association. Expression of the FZR1 point mutant results in accumulation of S-phase kinase-associated protein 2 (SKP2) and Polo-like kinase 1 (PLK1), while p27{sup Kip1} and p21{sup Cip1} proteins are downregulated, indicating a G1 cell cycle defect. Consistently, cells containing point mutant FZR1 enter the S phase prematurely. Together our results suggest that the LxCxD motif of FZR1 is a critical determinant for the interaction between FZR1 and pRB and is important for G1 restriction.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Changes in the C-terminal, N-terminal, and histidine regions of amelogenin reveal the role of oligomer quaternary structure on adsorption and hydroxyapatite mineralization

Adsorption interactions between amelogenin and calcium phosphate minerals are believed to be important to amelogenin’s function in enamel formation, however, the role of specific amino acid residues and domains within the protein in controlling adsorption is not well known. We synthesized “mechanistic probes” by systematically removing charged regions of amelogenin in order to elucidate their roles. The probes included amelogenin without the charged residues in the N-terminus (SEKR), without two, three, or eight histidines (H) in the central protein region (H2, H3, H8), or without the C-terminal residues (Delta). In-situ atomic force microscopy (AFM) adsorption studies onto hydroxyapatite (HAP) single crystals confirmed that the C-terminus was the dominant domain in promoting adsorption. We propose that subtle changes in protein-protein interactions for proteins with histidines and N-terminal residues removed resulted in changes in the oligomer quaternary size and structure that also affected protein adsorption. HAP mineralization studies revealed that the oligomer-HAP binding energy and protein layer thickness were factors in controlling the amorphous calcium phosphate (ACP) to HAP induction time. Our studies with mechanistic probes reveal the importance of the oligomer quaternary structure in controlling amelogenin adsorption and HAP mineralization.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Catalytic Bias in Enzymatic Metal Cofactor-Based Oxidation-Reduction Catalysis

Catalytic bias refers to the relative rate preference of a catalyst for either the forward or reverse direction. In enzymatic metal cofactor-based oxidation-reduction catalysis, the tuning of catalytic bias plays an underlying role in controlling rates of reactivity. For this, enzymes have evolved complex active sites that can exist in multiple oxidation states with differing reduction potentials in order to achieve challenging multi-step, oxidation-reduction reactions. Conceivably, the relative stability of the intermediates that contribute to determining the rate-limiting step of the catalytic cycle could impose catalytic bias, although mechanisms for this concept are just beginning to be realized. As one example, recent work on Clostridium pasteurianum [FeFe]-hydrogenases which catalyze reversible hydrogen oxidation have shown that the differential stabilization/destabilization of active site oxidation states through either static or dynamic protein interactions can preferentially promote either the hydrogen oxidation or proton reduction direction of the reaction. This revealed how an enzymatic cofactor can impose bias in oxidation-reduction catalysis through various tuning mechanisms by protein scaffold interactions. The hypothesis based on achieving catalytic bias through the modulation of cofactor oxidation states critical for the reaction cycle can be extended more generally to other cofactor-based oxidation-reductions catalysts. The current understanding of catalytic bias has significant implications for the design of synthetic catalysts used in industrial settings, as well as providing a greater fundamental understanding of the factors that control metabolic processes in all life.

catalysis↗
DOE OSTI2022

Better together: Protein partnerships for lineage-specific oil accumulation

Plant-derived oils are a major agricultural product that exist in both ubiquitous forms such as common vegetable oils and in specialized forms such as castor oil and coconut oil. These specialized oils are the result of lineage-specific metabolic pathways that create oils rich in unusual fatty acids. Considerable progress has been made toward understanding the enzymes that mediate fatty acid biosynthesis, triacylglycerol assembly, and oil storage. However, efforts to translate this knowledge into renewable bioproducts via engineered oil-producing plants and algae have had limited success. Here, we review recent evidence that protein–protein interactions in each of the three major phases of oil formation appear to have profound effects on specialized oil accumulation. We suggest that furthering our knowledge of the noncatalytic attributes of enzymes and other proteins involved in oil formation will be a critical step toward creating renewable bioproducts derived from high performing, engineered oilseeds.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Lectin Receptor-like Kinase Signaling during Engineered Ectomycorrhiza Colonization

Mutualistic association can improve a plant’s health and productivity. G-type lectin receptor-like kinase (PtLecRLK1) is a susceptibility factor in Populus trichocarpa that permits root colonization by a beneficial fungus, Laccaria bicolor. Engineering PtLecRLK1 also permits L. bicolor root colonization in non-host plants similar to Populus trichocarpa. The intracellular signaling reprogramed by PtLecRLK1 upon recognition of L. bicolor to allow for the development and maintenance of symbiosis is yet to be determined. In this study, phosphoproteomics was utilized to identify phosphorylation-based relevant signaling pathways associated with PtLecRLK1 recognition of L. bicolor in transgenic switchgrass roots. Our finding shows that PtLecRLK1 in transgenic plants modifies the chitin-triggered plant defense and MAPK signaling along with a significant adjustment in phytohormone signaling, ROS balance, endocytosis, cytoskeleton movement, and proteasomal degradation in order to facilitate the establishment and maintenance of L. bicolor colonization. Moreover, protein–protein interaction data implicate a cGMP-dependent protein kinase as a potential substrate of PtLecRLK1.

ectomycorrhiza colonization↗
DOE OSTI2022

Kinetic frustration by limited bond availability controls the LAT protein condensation phase transition on membranes

LAT is a membrane-linked scaffold protein that undergoes a phase transition to form a two-dimensional protein condensate on the membrane during T cell activation. Governed by tyrosine phosphorylation, LAT recruits various proteins that ultimately enable condensation through a percolation network of discrete and selective protein-protein interactions. Here, we describe detailed kinetic measurements of the phase transition, along with coarse-grained model simulations, that reveal that LAT condensation is kinetically frustrated by the availability of bonds to form the network. Unlike typical miscibility transitions in which compact domains may coexist at equilibrium, the LAT condensates are dynamically arrested in extended states, kinetically trapped out of equilibrium. Modeling identifies the structural basis for this kinetic arrest as the formation of spindle arrangements, favored by limited multivalent binding interactions along the flexible, intrinsically disordered LAT protein. These results reveal how local factors controlling the kinetics of LAT condensation enable formation of different, stable condensates, which may ultimately coexist within the cell.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Novel interaction interfaces mediate the interaction between the NEIL1 DNA glycosylase and mitochondrial transcription factor A

The maintenance of human mitochondrial DNA (mtDNA) is critical for proper cellular function as damage to mtDNA, if left unrepaired, can lead to a diverse array of pathologies. Of the pathways identified to participate in DNA repair within the mitochondria, base excision repair (BER) is the most extensively studied. Protein-protein interactions drive the step-by-step coordination required for the successful completion of this pathway and are important for crosstalk with other mitochondrial factors involved in genome maintenance. Human NEIL1 is one of seven DNA glycosylases that initiates BER in both the nuclear and mitochondrial compartments. In the current work, we scrutinized the interaction between NEIL1 and mitochondrial transcription factor A (TFAM), a protein that is essential for various aspects of mtDNA metabolism. We note, for the first time, that both the N- and C- terminal domains of NEIL1 interact with TFAM revealing a unique NEIL1 protein-binding interface. The interaction between the two proteins, as observed biochemically, appears to be transient and is most apparent at concentrations of low salt. The presence of DNA (or RNA) also positively influences the interaction between the two proteins, and molar mass estimates indicate that duplex DNA is required for complex formation at higher salt concentrations. Hydrogen deuterium exchange mass spectrometry data reveal that both proteins exchange less deuterium upon DNA binding, indicative of an interaction, and the addition of NEIL1 to the TFAM-DNA complex alters the interaction landscape. The transcriptional activity of TFAM appears to be independent of NEIL1 expression under normal cellular conditions, however, in the presence of DNA damage, we observe a significant reduction in the mRNA expression of TFAM-transcribed mitochondrial genes in the absence of NEIL1. Overall, our data indicate that the interaction between NEIL1 and TFAM can be modulated by local environment such as salt concentrations, protein availability, the presence of nucleic acids, as well as the presence of DNA damage.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

BIPSPI+: Mining Type-Specific Datasets of Protein Complexes to Improve Protein Binding Site Prediction

Computational approaches for predicting protein-protein interfaces are extremely useful for understanding and modelling the quaternary structure of protein assemblies. In particular, partner-specific binding site prediction methods allow delineating the specific residues that compose the interface of protein complexes. In recent years, new machine learning and other algorithmic approaches have been proposed to solve this problem. However, little effort has been made in finding better training datasets to improve the performance of these methods. With the aim of vindicating the importance of the training set compilation procedure, in this work we present BIPSPI+, a new version of our original server trained on carefully curated datasets that outperforms our original predictor. We show how prediction performance can be improved by selecting specific datasets that better describe particular types of protein interactions and interfaces (e.g. homo/hetero). In addition, our upgraded web server offers a new set of functionalities such as the sequence-structure prediction mode, hetero- or homo-complex specialization and the guided docking tool that allows to compute 3D quaternary structure poses using the predicted interfaces. BIPSPI+ is freely available at https://bipspi.cnb.csic.es.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

High-throughput small-angle X-ray scattering reveals effective structure factor transitions linked to high-concentration antibody viscosity

High-concentration monoclonal antibody (mAb) formulations are often constrained by elevated viscosity, largely driven by protein–protein interactions, which complicates manufacturing and limits subcutaneous delivery. Early viscosity risk assessment is essential during discovery, yet traditional measurements require large sample volumes, and lack high-throughput capability. Here, we develop a high-throughput small-angle X-ray scattering (SAXS) protocol to detect mAb self-association at dilute concentrations, enabling early predictive insights into high-concentration viscosity. Synchrotron SAXS measurements were conducted for 21 mAbs formulated in a histidine buffer at pH 6.0. An initial subset of 10 mAbs analyzed across 1–150 mg/mL revealed that effective structure factor transitions in the low-q region, indicative of interparticle interactions, consistently emerged below 25 mg/mL. Subsequently, 11 additional mAbs were analyzed at 1–25 mg/mL using automated liquid handling and flow cells to enable high-throughput screening. High-viscosity mAbs exhibited detectable low-q upturns at concentrations ≤10 mg/mL, whereas low-viscosity mAbs showed downturns. A classification criterion based on effective structure factor transitions accurately classified all high- and low-viscosity mAbs at 150 mg/mL, offering a scalable, sample-efficient alternative to conventional methods. These results extend recent findings on the concentration-dependent sensitivity of SAXS to short-range attractions, demonstrating that they can emerge at lower concentrations than previously reported. This study presents the most comprehensive and diverse SAXS dataset for mAbs reported to date within a single formulation, providing a valuable resource for developing and validating coarse-grained models that can more accurately capture intermolecular interactions governing high-concentration solution behavior, thereby enabling rational antibody engineering and improved developability.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Leveraging Artificial Intelligence to Expedite Antibody Design and Enhance Antibody–Antigen Interactions

This perspective sheds light on the transformative impact of recent computational advancements in the field of protein therapeutics, with a particular focus on the design and development of antibodies. Cutting-edge computational methods have revolutionized our understanding of protein–protein interactions (PPIs), enhancing the efficacy of protein therapeutics in preclinical and clinical settings. Central to these advancements is the application of machine learning and deep learning, which offers unprecedented insights into the intricate mechanisms of PPIs and facilitates precise control over protein functions. Despite these advancements, the complex structural nuances of antibodies pose ongoing challenges in their design and optimization. Our review provides a comprehensive exploration of the latest deep learning approaches, including language models and diffusion techniques, and their role in surmounting these challenges. We also present a critical analysis of these methods, offering insights to drive further progress in this rapidly evolving field. The paper includes practical recommendations for the application of these computational techniques, supplemented with independent benchmark studies. These studies focus on key performance metrics such as accuracy and the ease of program execution, providing a valuable resource for researchers engaged in antibody design and development. Through this detailed perspective, we aim to contribute to the advancement of antibody design, equipping researchers with the tools and knowledge to navigate the complexities of this field.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Artificial intelligence methods for protein structure and interaction prediction: Recent advances and challenges

Recent advances in artificial intelligence have introduced novel methods for high-accuracy prediction of protein tertiary structures, protein complex structures, and interactions between proteins and other biomolecules, such as small molecules and nucleic acids. Such advancements are accelerating biomedical research and the development of new protein design and bioengineering methods among many other important biotechnology applications. Here, in this review, we outline the recent advances in protein-centric biomolecular structure and interaction prediction, highlight some major challenges in the field, and discuss potential directions to address them.

Morehead, Alex [Lawrence Berkeley National Laborat↗