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At least 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
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↗
NASA NTRS2004

Environmental impacts on the developing CNS: CD15, NCAM-L1, and GFAP expression in rat neonates exposed to hypergravity

We have previously reported that the developing rat cerebellum is affected by hypergravity exposure. The effect is observed during a period of both granule and glial cell proliferation and neuronal migration in the cerebellum and coincides with changes in thyroid hormone levels. The present study begins to address the molecular mechanisms involved in the cerebellar response to hypergravity. Specifically, the study focuses on the expression of cerebellar proteins that are known to be directly involved in cell-cell interactions [protein expressing 3-fucosyl-N-acetyl-lactosamine antigen (CD15), neuronal cell adhesion molecule (NCAM-L1)] and those that affect cell-cell interactions indirectly [glial fibrillary acidic protein (GFAP)] in rat neonates exposed to centrifuge-produced hypergravity. Cerebellar mass and protein expression in rat neonates exposed to hypergravity (1.5 G) from gestational day (G) 11 to postnatal day (P) 30 were compared at one of six time points between P6 and P30 against rat neonates developing under normal gravity. Proteins were analyzed by quantitative western blots of cerebellar homogenates prepared from male or female neonates. Cerebellar size was most clearly reduced in male neonates on P6 and in female neonates on P9, with a significant gender difference; differences in cerebellar mass remained significant even when change in total body mass was factored in. Densitometric analysis of western blots revealed both quantitative and temporal changes in the expression of selected cerebellar proteins that coincided with changes in cerebellar mass and were gender-specific. In fact, our data indicated certain significant differences even between male and female control animals. A maximal decrease in expression of CD15 was observed in HG females on P9, coinciding with maximal change in their cerebellar mass. A shift in the time-course of NCAM-L1 expression resulted in a significant increase in NCAM-L1 in HG males on P18, an isolated time at which cerebellar mass does not significantly differ between HG and SC neonates. A maximal decrease in expression of GFAP was observed in HG males on P6, coinciding with maximal change in their cerebellar mass. Altered expression of cerebellar proteins is likely to affect a number of developmental processes and contribute to the structural and functional alterations seen in the CNS developing under altered gravity. Our data suggest that both cerebellar development and its response to gravitational manipulations differ in males and females. c2004 COSPAR. Published by Elsevier Ltd. All rights reserved.

NASA Program Fundamental Space Biology↗
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↗
NASA NTRS2001

Palmitoylation of caveolin-1 in endothelial cells is post-translational but irreversible

Caveolin-1 is a palmitoylated protein involved in assembly of signaling molecules in plasma membrane subdomains termed caveolae and in intracellular cholesterol transport. Three cysteine residues in the C terminus of caveolin-1 are subject to palmitoylation, which is not necessary for caveolar targeting of caveolin-1. Protein palmitoylation is a post-translational and reversible modification that may be regulated and that in turn may regulate conformation, membrane association, protein-protein interactions, and intracellular localization of the target protein. We have undertaken a detailed analysis of [(3)H]palmitate incorporation into caveolin-1 in aortic endothelial cells. The linkage of palmitate to caveolin-1 was hydroxylamine-sensitive and thus presumably a thioester bond. However, contrary to expectations, palmitate incorporation was blocked completely by the protein synthesis inhibitors cycloheximide and puromycin. In parallel experiments to show specificity, palmitoylation of aortic endothelial cell-specific nitric-oxide synthase was unaffected by these reagents. Inhibitors of protein trafficking, brefeldin A and monensin, blocked caveolin-1 palmitoylation, indicating that the modification was not cotranslational but rather required caveolin-1 transport from the endoplasmic reticulum and Golgi to the plasma membrane. In addition, immunophilin chaperones that form complexes with caveolin-1, i.e. FK506-binding protein 52, cyclophilin A, and cyclophilin 40, were not necessary for caveolin-1 palmitoylation because agents that bind immunophilins did not inhibit palmitoylation. Pulse-chase experiments showed that caveolin-1 palmitoylation is essentially irreversible because the release of [(3)H]palmitate was not significant even after 24 h. These results show that [(3)H]palmitate incorporation is limited to newly synthesized caveolin-1, not because incorporation only occurs during synthesis but because the continuous presence of palmitate on caveolin-1 prevents subsequent repalmitoylation.

Non-NASA Center↗
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↗
NASA NTRS2022

Resolving the fine structure in the energy landscapes of repeat proteins

Ankyrin (ANK) repeat proteins are coded by tandem occurrences of patterns with around 33 amino acids. They often mediate protein–protein interactions in a diversity of biological systems. These proteins have an elongated non-globular shape and often display complex folding mechanisms. This work investigates the energy landscape of representative proteins of this class made up of 3, 4 and 6 ANK repeats using the energy-landscape visualisation method (ELViM). By combining biased and unbiased coarse-grained molecular dynamics AWSEM simulations that sample conformations along the folding trajectories with the ELViM structure-based phase space, one finds a three-dimensional representation of the globally funnelled energy surface. In this representation, it is possible to delineate distinct folding pathways. We show that ELViMs can project, in a natural way, the intricacies of the highly dimensional energy landscapes encoded by the highly symmetric ankyrin repeat proteins into useful low-dimensional representations. These projections can discriminate between multiplicities of specific parallel folding mechanisms that otherwise can be hidden in oversimplified depictions.

Murilo N. Sanches↗
NASA NTRS1999

ARG1 (altered response to gravity) encodes a DnaJ-like protein that potentially interacts with the cytoskeleton

Gravitropism allows plant organs to direct their growth at a specific angle from the gravity vector, promoting upward growth for shoots and downward growth for roots. Little is known about the mechanisms underlying gravitropic signal transduction. We found that mutations in the ARG1 locus of Arabidopsis thaliana alter root and hypocotyl gravitropism without affecting phototropism, root growth responses to phytohormones or inhibitors of auxin transport, or starch accumulation. The positional cloning of ARG1 revealed a DnaJ-like protein containing a coiled-coil region homologous to coiled coils found in cytoskeleton-interacting proteins. These data suggest that ARG1 participates in a gravity-signaling process involving the cytoskeleton. A combination of Northern blot studies and analysis of ARG1-GUS fusion-reporter expression in transgenic plants demonstrated that ARG1 is expressed in all organs. Ubiquitous ARG1 expression in Arabidopsis and the identification of an ortholog in Caenorhabditis elegans suggest that ARG1 is involved in other essential processes.

Non-NASA Center↗
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↗
DOE OSTI2024

Structure of HIV-1 RRE stem-loop II identifies two conformational states of the high-affinity Rev binding site

During HIV infection, specific RNA-protein interaction between the Rev response element (RRE) and viral Rev protein is required for nuclear export of intron-containing viral mRNA transcripts. Rev initially binds the high-affinity site in stem-loop II, which promotes oligomerization of additional Rev proteins on RRE. Here, we present the crystal structure of RRE stem-loop II in distinct closed and open conformations. The high-affinity Rev-binding site is located within the three-way junction rather than the predicted stem IIB. The closed and open conformers differ in their non-canonical interactions within the three-way junction, and only the open conformation has the widened major groove conducive to initial Rev interaction. Rev binding assays show that RRE stem-loop II has high- and low-affinity binding sites, each of which binds a Rev dimer. We propose a binding model, wherein Rev-binding sites on RRE are sequentially created through structural rearrangements induced by Rev-RRE interactions.

60 APPLIED LIFE SCIENCES↗
NASA NTRS1994

Pr-specific phytochrome phosphorylation in vitro by a protein kinase present in anti-phytochrome maize immunoprecipitates

Protein kinase activity has repeatedly been found to co-purify with the plant photoreceptor phytochrome, suggesting that light signals received by phytochrome may be transduced or modulated through protein phosphorylation. In this study immunoprecipitation techniques were used to characterize protein kinase activity associated with phytochrome from maize (Zea mays L.). A protein kinase that specifically phosphorylated phytochrome was present in washed anti-phytochrome immunoprecipitates of etiolated coleoptile proteins. No other substrate tested was phosphorylated by this kinase. Adding salts or detergents to disrupt low-affinity protein interactions reduced background phosphorylation in immunoprecipitates without affecting phytochrome phosphorylation, indicating that the protein kinase catalytic activity is either intrinsic to the phytochrome molecule or associated with it by high-affinity interactions. Red irradiation (of coleoptiles or extracts) sufficient to approach photoconversion saturation reduced phosphorylation of immunoprecipitated phytochrome. Subsequent far-red irradiation reversed the red-light effect. Phytochrome phosphorylation was stimulated about 10-fold by a co-immunoprecipitated factor. The stimulatory factor was highest in immunoprecipitates when Mg2+ was present in immunoprecipitation reactions but remained in the supernatant in the absence of Mg2+. These observations provide strong support for the hypothesis that phytochrome-associated protein kinase modulates light responses in vivo. Since only phytochrome was found to be phosphorylated, the co-immunoprecipitated protein kinase may function to regulate receptor activity.

NASA Discipline Plant Biology↗
DOE OSTI2023

Bioexcitons by Design: How Do We Get There?

Biological pigment-protein complexes (PPCs) exhibit a remarkable ability to tune the optical properties of biological excitons (bioexcitons) through specific pigment-protein interactions. While such fine-tuning allows natural systems (e.g., photosynthetic proteins) to carry out their native functions with near-optimal performance, native function itself is often suboptimal for applications such as biofuel production or quantum technology development. Furthermore, this perspective offers a look at near-term prospects for the rational reoptimization of PPC bioexcitons for new functions using site-directed mutagenesis. The primary focus is on the "structure-spectrum" challenge of understanding the relationships between structural features and spectroscopic properties. While recent examples demonstrate that site-directed mutagenesis can be used to tune nearly all key bioexciton parameters (e.g., site energies, inter-pigment couplings, and electronic-vibrational interactions), critical challenges remain before we achieve truly rational design of bioexciton properties.

09 BIOMASS FUELS↗
DOE OSTI2022

Deep learning-driven insights into super protein complexes for outer membrane protein biogenesis in bacteria

To reach their final destinations, outer membrane proteins (OMPs) of gram-negative bacteria undertake an eventful journey beginning in the cytosol. Multiple molecular machines, chaperones, proteases, and other enzymes facilitate the translocation and assembly of OMPs. These helpers usually associate, often transiently, forming large protein assemblies. They are not well understood due to experimental challenges in capturing and characterizing protein-protein interactions (PPIs), especially transient ones. Using AF2Complex, we introduce a high-throughput, deep learning pipeline to identify PPIs within the Escherichia coli cell envelope and apply it to several proteins from an OMP biogenesis pathway. Among the top confident hits obtained from screening ~1500 envelope proteins, we find not only expected interactions but also unexpected ones with profound implications. Subsequently, we predict atomic structures for these protein complexes. These structures, typically of high confidence, explain experimental observations and lead to mechanistic hypotheses for how a chaperone assists a nascent, precursor OMP emerging from a translocon, how another chaperone prevents it from aggregating and docks to a β-barrel assembly port, and how a protease performs quality control. This work presents a general strategy for investigating biological pathways by using structural insights gained from deep learning-based predictions.

60 APPLIED LIFE SCIENCES↗
NASA NTRS2005

Water as a matrix for life

Life is based on non-covalent interactions. They might be either specific (enzyme-substrate interactions, selective ion transport) or nonspecific (lipid-lipid and lipid-protein interactions needed for membrane integrity, fusion and division). Their strength needs to be properly tuned, and this is mediated by the solvent. If interactions are too weak, there might be undesired response to natural fluctuations of physical and chemical parameters. If they are too strong it could impede kinetics and energetics of cellular processes. Thus, the solvent must allow for balancing these interactions. Physical and chemical properties of solvent provide strong constraints for life. Water exhibits a remarkable trait that it promotes both solvophobic and solvophilic interactions. Solvophobic interactions; related to high dielectric constant of the solvent) are necessary for self-organization of matter whereas solvophilic interactions are needed to ensure solubility of polar species. Water offers a large temperature domain of stable liquid and the characteristics hydrophobic effects are a consequence of the temperature in sensitivity of essential properties of its liquid state. Water, however, is not the only liquid with these favorable properties. I will compare in detail properties of water and other pure liquids or their mixtures that have a high dielectric constant and simultaneously support self-organization. I will also discuss properties of water that are unfavorable to life (e.g. its chemical activity against polymerization reactions) and close with summarizing what are alternatives to water as a matrix of life in space.

Pohorille, Andrew↗
DOE OSTI2023

Drug discovery efforts at George Mason University

With over 39,000 students, and research expenditures in excess of $200 million, George Mason University (GMU) is the largest R1 (Carnegie Classification of very high research activity) university in Virginia. Mason scientists have been involved in the discovery and development of novel diagnostics and therapeutics in areas as diverse as infectious diseases and cancer. Below are highlights of the efforts being led by Mason researchers in the drug discovery arena. To enable targeted cellular delivery, and non-biomedical applications, Veneziano and colleagues have developed a synthesis strategy that enables the design of self-assembling DNA nanoparticles (DNA origami) with prescribed shape and size in the 10 to 100 nm range. The nanoparticles can be loaded with molecules of interest such as drugs, proteins and peptides, and are a promising new addition to the drug delivery platforms currently in use. The investigators also recently used the DNA origami nanoparticles to fine tune the spatial presentation of immunogens to study the impact on B cell activation. These studies are an important step towards the rational design of vaccines for a variety of infectious agents. To elucidate the parameters for optimizing the delivery efficiency of lipid nanoparticles (LNPs), Buschmann, Paige and colleagues have devised methods for predicting and experimentally validating the pKa of LNPs based on the structure of the ionizable lipids used to formulate the LNPs. These studies may pave the way for the development of new LNP delivery vehicles that have reduced systemic distribution and improved endosomal release of their cargo post administration. To better understand protein-protein interactions and identify potential drug targets that disrupt such interactions, Luchini and colleagues have developed a methodology that identifies contact points between proteins using small molecule dyes. The dye molecules noncovalently bind to the accessible surfaces of a protein complex with very high affinity, but are excluded from contact regions. When the complex is denatured and digested with trypsin, the exposed regions covered by the dye do not get cleaved by the enzyme, whereas the contact points are digested. The resulting fragments can then be identified using mass spectrometry. The data generated can serve as the basis for designing small molecules and peptides that can disrupt the formation of protein complexes involved in disease processes. For example, using peptides based on the interleukin 1 receptor accessory protein (IL-1RAcP), Luchini, Liotta, Paige and colleagues disrupted the formation of IL-1/IL-R/IL-1RAcP complex and demonstrated that the inhibition of complex formation reduced the inflammatory response to IL-1B. Working on the discovery of novel antimicrobial agents, Bishop, van Hoek and colleagues have discovered a number of antimicrobial peptides from reptiles and other species. DRGN-1, is a synthetic peptide based on a histone H1-derived peptide that they had identified from Komodo Dragon plasma. DRGN-1 was shown to disrupt bacterial biofilms and promote wound healing in an animal model. The peptide, along with others, is being developed and tested in preclinical studies. Other research by van Hoek and colleagues focuses on in silico antimicrobial peptide discovery, screening of small molecules for antibacterial properties, as well as assessment of diffusible signal factors (DFS) as future therapeutics. The above examples provide insight into the cutting-edge studies undertaken by GMU scientists to develop novel methodologies and platform technologies important to drug discovery.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

DNCON2_Inter: predicting interchain contacts for homodimeric and homomultimeric protein complexes using multiple sequence alignments of monomers and deep learning

Deep learning methods that achieved great success in predicting intrachain residue-residue contacts have been applied to predict interchain contacts between proteins. However, these methods require multiple sequence alignments (MSAs) of a pair of interacting proteins (dimers) as input, which are often difficult to obtain because there are not many known protein complexes available to generate MSAs of sufficient depth for a pair of proteins. In recognizing that multiple sequence alignments of a monomer that forms homomultimers contain the co-evolutionary signals of both intrachain and interchain residue pairs in contact, we applied DNCON2 (a deep learning-based protein intrachain residue-residue contact predictor) to predict both intrachain and interchain contacts for homomultimers using multiple sequence alignment (MSA) and other co-evolutionary features of a single monomer followed by discrimination of interchain and intrachain contacts according to the tertiary structure of the monomer. We name this tool DNCON2_Inter. Allowing true-positive predictions within two residue shifts, the best average precision was obtained for the Top-L/10 predictions of 22.9% for homodimers and 17.0% for higher-order homomultimers. In some instances, especially where interchain contact densities are high, DNCON2_Inter predicted interchain contacts with 100% precision. We also developed Con_Complex, a complex structure reconstruction tool that uses predicted contacts to produce the structure of the complex. Using Con_Complex, we show that the predicted contacts can be used to accurately construct the structure of some complexes. Our experiment demonstrates that monomeric multiple sequence alignments can be used with deep learning to predict interchain contacts of homomeric proteins.

59 BASIC BIOLOGICAL SCIENCES↗