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At least 271 records · Page 15
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Multi-head attention-based U-Nets for predicting protein domain boundaries using 1D sequence features and 2D distance maps

Abstract The information about the domain architecture of proteins is useful for studying protein structure and function. However, accurate prediction of protein domain boundaries (i.e., sequence regions separating two domains) from sequence remains a significant challenge. In this work, we develop a deep learning method based on multi-head U-Nets (called DistDom) to predict protein domain boundaries utilizing 1D sequence features and predicted 2D inter-residue distance map as input. The 1D features contain the evolutionary and physicochemical information of protein sequences, whereas the 2D distance map includes the structural information of proteins that was rarely used in domain boundary prediction before. The 1D and 2D features are processed by the 1D and 2D U-Nets respectively to generate hidden features. The hidden features are then used by the multi-head attention to predict the probability of each residue of a protein being in a domain boundary, leveraging both local and global information in the features. The residue-level domain boundary predictions can be used to classify proteins as single-domain or multi-domain proteins. It classifies the CASP14 single-domain and multi-domain targets at the accuracy of 75.9%, 13.28% more accurate than the state-of-the-art method. Tested on the CASP14 multi-domain protein targets with expert annotated domain boundaries, the average per-target F1 measure score of the domain boundary prediction by DistDom is 0.263, 29.56% higher than the state-of-the-art method.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Dissecting the structural heterogeneity of proteins by native mass spectrometry

Abstract A single gene yields many forms of proteins via combinations of posttranscriptional/posttranslational modifications. Proteins also fold into higher‐order structures and interact with other molecules. The combined molecular diversity leads to the heterogeneity of proteins that manifests as distinct phenotypes. Structural biology has generated vast amounts of data, effectively enabling accurate structural prediction by computational methods. However, structures are often obtained heterologously under homogeneous states in vitro. The lack of native heterogeneity under cellular context creates challenges in precisely connecting the structural data to phenotypes. Mass spectrometry (MS) based proteomics methods can profile proteome composition of complex biological samples. Most MS methods follow the “bottom‐up” approach, which denatures and digests proteins into short peptide fragments for ease of detection. Coupled with chemical biology approaches, higher‐order structures can be probed via incorporation of covalent labels on native proteins that are maintained at the peptide level. Alternatively, native MS follows the “top‐down” approach and directly analyzes intact proteins under nondenaturing conditions. Various tandem MS activation methods can dissect the intact proteins for in‐depth structural elucidation. Herein, we review recent native MS applications for characterizing heterogeneous samples, including proteins binding to mixtures of ligands, homo/hetero‐complexes with varying stoichiometry, intrinsically disordered proteins with dynamic conformations, glycoprotein complexes with mixed modification states, and active membrane protein complexes in near‐native membrane environments. We summarize the benefits, challenges, and ongoing developments in native MS, with the hope to demonstrate an emerging technology that complements other tools by filling the knowledge gaps in understanding the molecular heterogeneity of proteins.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Assessing Structural, Thermal, and Functional Characteristics of Marigold Flower Protein as a Sustainable Food Ingredient

The demand for sustainable and alternative protein sources has been on the rise, driving interest in the valorization of underutilized plants. This study evaluated Calendula officinalis (marigold), a common floral waste, as a sustainable alternative protein source for the food industry. The primary objective of this study was to investigate the physicochemical properties of protein fractions from Calendula officinalis flower to evaluate their potential as a novel protein ingredient. Extraction of the Calendula officinalis flower yielded 92.17% of the crude protein. A sequential extraction of albumin, globulin, glutelin, and prolamin from marigold flower revealed albumin as the dominant fraction (65.47%) and exhibited the highest protein functionality, including water-holding capacity (2.37 g/g), oil-holding capacity (2.49 g/g), and emulsifying capacity (65.22 mL/g). Compared with other protein fractions, glutelin showed a relatively high emulsifying and foaming capacity (EC: 59.13 mL/g; FC: 16.23%). Differential scanning calorimetry revealed high thermal stability for albumin (T p = 105.28 °C) and glutelin (T p = 97.6 °C). Sodium Dodecyl Sulfate–Polyacrylamide Gel Electrophoresis (SDS-PAGE) and Liquid Chromatography–Mass Spectrometry (LC-MS) confirmed the presence of abundant low-molecular-weight polypeptides (<37 kDa), which enhanced emulsification, while scanning electron microscopy revealed porous structures aligned with hydration properties. Antioxidant activity was higher in albumin and glutelin, linked to surface hydrophobicity. LC-MS/MS identified 33 short-chain proteins, including oxidoreductase proteins and lipid-transfer proteins. Findings highlight marigold flower proteins as a sustainable, functional ingredient for a diverse range of food applications.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Population-based heteropolymer design to mimic protein mixtures

Biological fluids, the most complex blends, have compositions that constantly vary and cannot be molecularly defined. Despite these uncertainties, proteins fluctuate, fold, function and evolve as programmed. We propose that in addition to the known monomeric sequence requirements, protein sequences encode multi-pair interactions at the segmental level to navigate random encounters; synthetic heteropolymers capable of emulating such interactions can replicate how proteins behave in biological fluids individually and collectively. Here, we extracted the chemical characteristics and sequential arrangement along a protein chain at the segmental level from natural protein libraries and used the information to design heteropolymer ensembles as mixtures of disordered, partially folded and folded proteins. For each heteropolymer ensemble, the level of segmental similarity to that of natural proteins determines its ability to replicate many functions of biological fluids including assisting protein folding during translation, preserving the viability of fetal bovine serum without refrigeration, enhancing the thermal stability of proteins and behaving like synthetic cytosol under biologically relevant conditions. Molecular studies further translated protein sequence information at the segmental level into intermolecular interactions with a defined range, degree of diversity and temporal and spatial availability. This framework provides valuable guiding principles to synthetically realize protein properties, engineer bio/abiotic hybrid materials and, ultimately, realize matter-to-life transformations.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Repetitive proteins that undergo large conformational changes evade structural prediction algorithms

Protein structure prediction algorithms, such as AlphaFold, have accelerated protein design and advanced the understanding of the relationship between amino acid sequence and protein structure. However, these algorithms are limited in their ability to predict the structures of conformationally dynamic, intrinsically disordered, and stimuli-responsive proteins. To evaluate sequence-to-structure predictions of such challenging proteins, we explored a class of conformationally dynamic, repeats-in-toxin (RTX) proteins. RTX proteins adopt intrinsically disordered conformations in the absence of calcium and undergo reversible folding into β-roll structures upon binding to calcium. RTX proteins are characterized by tandem repeats of the sequence GGXGXDXUX, in which X can be any amino acid and U is an aliphatic amino acid. We designed RTX sequence variants with global substitutions of nonconserved amino acids, tandem repeats of consensus sequences GGAGXDTLY, and tandem repeats of scrambled sequences GGAGXDTYL. AlphaFold2 and AlphaFold3 predicted that all of these RTX variants adopt β-roll structures, characteristic of wild-type RTX bound to calcium. However, modeling the predicted structures with molecular dynamics simulations and characterizing the protein variants with circular dichroism spectroscopy, small-angle x-ray scattering, and x-ray crystallography revealed that variants adopt diverse, sequence-dependent structures in the absence and presence of calcium. To better design proteins for applications in biotechnology and sustainability, it is critical to build predictive tools that consider intrinsically disordered protein states and validate these tools with multi-mode, multi-scale experimental data.

Chang, Marina P. [Stanford Univ., CA (United State↗
DOE OSTI2020

Evolutionary Variation in MADS Box Dimerization Affects Floral Development and Protein Abundance in Maize

Interactions between MADS-box transcription factors are critical in the regulation of floral development, and shifting MADS-box protein-protein interactions are predicted to have influenced floral evolution. However, precisely how evolutionary variation in protein-protein interactions affects MADS-box protein function remains unknown. To assess the impact of changing MADS-box protein-protein interactions on transcription factor function, we turned to the grasses, where interactions between B-class MADS-box proteins vary. We tested the functional consequences of this evolutionary variability using maize as an experimental system. We found that differential B-class dimerization was associated with subtle, quantitative differences in stamen shape. In contrast, differential dimerization resulted in large-scale changes to downstream gene expression. Differential dimerization also affected B-class complex composition and abundance, independent of transcript levels. This indicates that differential B-class dimerization affects protein degradation, revealing an important consequence for evolutionary variability in MADS-box interactions. Our results highlight complexity in the evolution of developmental gene networks - changing protein-protein interactions could affect not only the composition of transcription factor complexes, but also their degradation and persistence in developing flowers. Here, our results also show how coding change in a pleiotropic master regulator could have small, quantitative effects on development.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

A dynamic protein interactome drives energy conservation and electron flux in Thermococcus kodakarensis

ABSTRACT Life is supported by energy gains fueled by catabolism of a wide range of substrates, each reliant on the selective partitioning of electrons through redox ( red uction and ox idation) reactions. Electron flux through tunable and regulated protein interactions provides dynamic routes for energy conservation, but how electron flux is regulated in vivo , particularly for archaeal metabolisms that support rapid growth at the thermodynamic limits of life, is poorly understood. Identification of bona fide in vivo protein assemblies and how such assemblies dictate the totality of electron flux is critical to our understanding of the regulation imposed on metabolism, energy production, and energy conservation. Here, 25 key proteins in central metabolic redox pathways in the model, genetically accessible, hyperthermophilic archaeon Thermococcus kodakarensis , were purified to reveal an extensive, dynamic, and tightly interconnected network of protein interactions that responds to environmental cues (such as the availability of various reductive sinks) to direct electron flux to maximize energetic gains. Interactions connecting disparate functions suggest many catabolic and anabolic activities occur in spatial proximity in vivo , and while protein complexes have been historically defined under optimal conditions, many of these complexes appear to maintain alternative partnerships in changing conditions. The totality of the results obtained redefines our understanding of in vivo assemblies driving ancient metabolic strategies supporting the growth of modern Archaea. IMPORTANCE Given the potential for rational genetic manipulations of biofuel- and biotech-promising archaea to yield transformative results for major markets, it is a priority to define how the metabolisms of such species are controlled, at least in part, by in vivo protein assemblies, and from such, define routes of energy flux that can be most efficiently altered toward biofuel or biotechnological gains. Proteinaceous electron carriers (PECs, such as ferredoxins) offer the potential for specific protein–protein interactions to coordinate selective reductive flow. Employing the model, genetically accessible, hyperthermophilic archaeon, Thermococcus kodakarensis , we establish the metabolic protein interactome of 25 key redox proteins, revealing that each redox active protein has a dynamic partnership profile, suggesting catabolic and anabolic activities may occur in concert and in temporal and spatial proximity in vivo . These results reveal critical importance in evaluating the newly identified partnerships and their role and utility in providing regulated redox flux in T. kodakarensis .

Williams, Sere A. (ORCID:0000000235509590)↗
DOE OSTI2020

Quantitative Proteome Profiling Reveals Cellobiose-Dependent Protein Processing and Export Pathways for the Lignocellulolytic Response in Neurospora crassa

Filamentous fungi are intensively used for producing industrial enzymes, including lignocellulases. Employing insoluble cellulose to induce the production of lignocellulases causes some drawbacks, e.g., a complex fermentation operation, which can be overcome by using soluble inducers such as cellobiose. Here, a triple β-glucosidase mutant of Neurospora crassa, which prevents rapid turnover of cellobiose and thus allows the disaccharide to induce lignocellulases, was applied to profile the proteome responses to cellobiose and cellulose (Avicel). Our results revealed a shared proteomic response to cellobiose and Avicel, whose elements included lignocellulases and cellulolytic product transporters. While the cellulolytic proteins showed a correlated increase in protein and mRNA levels, only a moderate correlation was observed on a proteomic scale between protein and mRNA levels (R 2 = 0.31). Ribosome biogenesis and rRNA processing were significantly overrepresented in the protein set with increased protein but unchanged mRNA abundances in response to Avicel. Ribosome biogenesis, as well as protein processing and protein export, was also enriched in the protein set that showed increased abundance in response to cellobiose. NCU05895, a homolog of yeast CWH43, is potentially involved in transferring a glycosylphosphatidylinositol (GPI) anchor to nascent proteins. This protein showed increased abundance but no significant change in mRNA levels. Disruption of CWH43 resulted in a significant decrease in cellulase activities and secreted protein levels in cultures grown on Avicel, suggesting a positive regulatory role for CWH43 in cellulase production. The findings should have an impact on a systems engineering approach for strain improvement for the production of lignocellulases.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Exploring the fragmentation efficiency of proteins analyzed by MALDI-TOF-TOF tandem mass spectrometry using computational and statistical analyses

Matrix-assisted laser desorption/ionization time-of-flight-time-of-flight (MALDI-TOF-TOF) tandem mass spectrometry (MS/MS) is a rapid technique for identifying intact proteins from unfractionated mixtures by top-down proteomic analysis. MS/MS allows isolation of specific intact protein ions prior to fragmentation, allowing fragment ion attribution to a specific precursor ion. However, the fragmentation efficiency of mature, intact protein ions by MS/MS post-source decay (PSD) varies widely, and the biochemical and structural factors of the protein that contribute to it are poorly understood. With the advent of protein structure prediction algorithms such as Alphafold2, we have wider access to protein structures for which no crystal structure exists. In this work, we use a statistical approach to explore the properties of bacterial proteins that can affect their gas phase dissociation via PSD. We extract various protein properties from Alphafold2 predictions and analyze their effect on fragmentation efficiency. Our results show that the fragmentation efficiency from cleavage of the polypeptide backbone on the C-terminal side of glutamic acid (E) and asparagine (N) residues were nearly equal. In addition, we found that the rearrangement and cleavage on the C-terminal side of aspartic acid (D) residues that result from the aspartic acid effect (AAE) were higher than for E- and N-residues. From residue interaction network analysis, we identified several local centrality measures and discussed their implications regarding the AAE. We also confirmed the selective cleavage of the backbone at D-proline bonds in proteins and further extend it to N-proline bonds. Finally, we note an enhancement of the AAE mechanism when the residue on the C-terminal side of D-, E- and N-residues is glycine. To the best of our knowledge, this is the first report of this phenomenon. Our study demonstrates the value of using statistical analyses of protein sequences and their predicted structures to better understand the fragmentation of the intact protein ions in the gas phase.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Deep Learning Prediction of Protein Complex Structures

Proteins interact to form protein complex to carry out biological functions such as catalytic chemical reaction. Therefore, it is important to develop computational methods to predict protein-protein interaction and the structures of protein complexes to study and enhance protein function. In this project, we successfully developed several deep learning methods to predict inter-protein contacts and the reinforcement learning and optimization methods to reconstruct protein complex structures from predicted inter-chain contacts. The methods were integrated with the MULTICOM protein complex structure prediction system and applied to predict the complex structures of biomass production-related proteins of green algae. During the two and a half years of research and development, all the specific milestones of the project were achieved successfully. 16 publications/manuscripts were produced. 10 software tools were developed. A patent application was submitted. Our MULTICOM predictors leveraging some tools developed in this project were ranked among the top predictors in the 15th Critical Assessment of Techniques for Protein Structure Prediction (CASP15) in 2022.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Phloem Exudate Protein Profiles during Drought and Recovery Reveal Abiotic Stress Responses in Tomato Vasculature

Drought is the leading cause of agricultural yield loss among all abiotic stresses, and the link between water deficit and phloem protein contents is relatively unexplored. Here we collected phloem exudates from Solanum lycopersicum leaves during periods of drought stress and recovery. Our analysis identified 2558 proteins, the most abundant of which were previously localized to the phloem. Independent of drought, enrichment analysis of the total phloem exudate protein profiles from all samples suggests that the protein content of phloem sap is complex, and includes proteins that function in chaperone systems, branched-chain amino acid synthesis, trehalose metabolism, and RNA silencing. We observed 169 proteins whose abundance changed significantly within the phloem sap, either during drought or recovery. Proteins that became significantly more abundant during drought include members of lipid metabolism, chaperone-mediated protein folding, carboxylic acid metabolism, abscisic acid signaling, cytokinin biosynthesis, and amino acid metabolism. Conversely, proteins involved in lipid signaling, sphingolipid metabolism, cell wall organization, carbohydrate metabolism, and a mitogen-activated protein kinase are decreased during drought. Our experiment has achieved an in-depth profiling of phloem sap protein contents during drought stress and recovery that supports previous findings and provides new evidence that multiple biological processes are involved in drought adaptation.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS2000

Technical advance: identification of plant actin-binding proteins by F-actin affinity chromatography

Proteins that interact with the actin cytoskeleton often modulate the dynamics or organization of the cytoskeleton or use the cytoskeleton to control their localization. In plants, very few actin-binding proteins have been identified and most are thought to modulate cytoskeleton function. To identify actin-binding proteins that are unique to plants, the development of new biochemical procedures will be critical. Affinity columns using actin monomers (globular actin, G-actin) or actin filaments (filamentous actin, F-actin) have been used to identify actin-binding proteins from a wide variety of organisms. Monomeric actin from zucchini (Cucurbita pepo L.) hypocotyl tissue was purified to electrophoretic homogeneity and shown to be native and competent for polymerization to actin filaments. G-actin, F-actin and bovine serum albumin affinity columns were prepared and used to separate samples enriched in either soluble or membrane-associated actin-binding proteins. Extracts of soluble actin-binding proteins yield distinct patterns when eluted from the G-actin and F-actin columns, respectively, leading to the identification of a putative F-actin-binding protein of approximately 40 kDa. When plasma membrane-associated proteins were applied to these columns, two abundant polypeptides eluted selectively from the F-actin column and cross-reacted with antiserum against pea annexins. Additionally, a protein that binds auxin transport inhibitors, the naphthylphthalamic acid binding protein, which has been previously suggested to associate with the actin cytoskeleton, was eluted in a single peak from the F-actin column. These experiments provide a new approach that may help to identify novel actin-binding proteins from plants.

Non-NASA Center↗
NASA NTRS2007

Benchtop Detection of Proteins

A process, and a benchtop-scale apparatus for implementing the process, have been developed to detect proteins associated with specific microbes in water. The process and apparatus may also be useful for detection of proteins in other, more complex liquids. There may be numerous potential applications, including monitoring lakes and streams for contamination, testing of blood and other bodily fluids in medical laboratories, and testing for microbial contamination of liquids in restaurants and industrial food-processing facilities. A sample can be prepared and analyzed by use of this process and apparatus within minutes, whereas an equivalent analysis performed by use of other processes and equipment can often take hours to days. The process begins with the conjugation of near-infrared-fluorescent dyes to antibodies that are specific to a particular protein. Initially, the research has focused on using near-infrared dyes to detect antigens or associated proteins in solution, which has proven successful vs. microbial cells, and streamlining the technique in use for surface protein detection on microbes would theoretically render similar results. However, it is noted that additional work is needed to transition protein-based techniques to microbial cell detection. Consequently, multiple such dye/antibody pairs could be prepared to enable detection of multiple selected microbial species, using a different dye for each species. When excited by near-infrared light of a suitable wavelength, each dye fluoresces at a unique longer wavelength that differs from those of the other dyes, enabling discrimination among the various species. In initial tests, the dye/antibody pairs are mixed into a solution suspected of containing the selected proteins, causing the binding of the dye/antibody pairs to such suspect proteins that may be present. The solution is then run through a microcentrifuge that includes a membrane that acts as a filter in that it retains the dye/antibody/protein complexes while allowing any remaining unbound dye/antibody pairs to flow away. The retained dye/antibody/protein complexes are transferred to a cuvette, wherein they are irradiated with light from a miniature near-infrared laser delivered via a fiber-optic cable. The resulting fluorescence from the dye(s) is measured by use of a miniature spectrometer, the output of which is digitized, then analyzed by laptop computer. The software running in the computer identifies the protein species by the wavelengths of their spectral peaks and determines the amounts of the proteins, and thus, one day, microbes of the various species from the intensities of the peaks. The abovementioned removal of the unbound dye/antibody pairs during centrifugation prevents false positive readings. The process proves successful in detecting proteins in solution and thus can now be employed for use in microbe detection.

Scardelletti, Maximilian C.↗
DOE OSTI2023

Identification and preliminary characterization of conserved uncharacterized proteins from Chlamydomonas reinhardtii , Arabidopsis thaliana , and Setaria viridis

Abstract The rapid accumulation of sequenced plant genomes in the past decade has outpaced the still difficult problem of genome‐wide protein‐coding gene annotation. A substantial fraction of protein‐coding genes in all plant genomes are poorly annotated or unannotated and remain functionally uncharacterized. We identified unannotated proteins in three model organisms representing distinct branches of the green lineage (Viridiplantae): Arabidopsis thaliana (eudicot), Setaria viridis (monocot), and Chlamydomonas reinhardtii (Chlorophyte alga). Using similarity searching, we identified a subset of unannotated proteins that were conserved between these species and defined them as Deep Green proteins. Bioinformatic, genomic, and structural predictions were performed to begin classifying Deep Green genes and proteins. Compared to whole proteomes for each species, the Deep Green set was enriched for proteins with predicted chloroplast targeting signals predictive of photosynthetic or plastid functions, a result that was consistent with enrichment for daylight phase diurnal expression patterning. Structural predictions using AlphaFold and comparisons to known structures showed that a significant proportion of Deep Green proteins may possess novel folds. Though only available for three organisms, the Deep Green genes and proteins provide a starting resource of high‐value targets for further investigation of potentially new protein structures and functions conserved across the green lineage.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Selective Enrichment Coupled with Proteomics to Identify S-Acylated Plasma Membrane Proteins in Arabidopsis

Protein S-acylation, predominately in the form of palmitoylation, is a reversible lipid post-translational modification (PTM) on cysteines that plays important roles in protein localization, trafficking, activity, and complex assembly. The functions and regulatory mechanisms of Sacylation have been extensively studied in mammals, owing to remarkable development of the high-resolution proteomics and the discovery of the S-acylation-related enzymes. However, the advance of S-acylation studies in plants lags farther behind than that in mammals, mainly due to the lack of knowledge about proteins responsible for this process, such as protein acyltransferases (PATs) and their substrates. In this article, a systematic protocol to study global S-acylation in Arabidopsis seedlings is described. The procedures are presented in detail, including preparation of Arabidopsis seedlings, enrichment of plasma membrane (PM) proteins, followed by enrichment of S-acylated proteins/peptides based on the acyl-biotin exchange (ABE) method, and large-scale identification of S-acylated proteins/peptides via mass spectrometry. This approach enables researchers to study S-acylation of PM proteins in plants in a systematic and straightforward way. Basic protocol 1: Preparation of Arabidopsis seedling materials Basic protocol 2: Isolation and enrichment of plasma membrane proteins Support protocol 1: Determination of protein concentration using BCA assay Basic Protocol 3: Enrichment of S-acylated proteins by ABE method Support protocol 2: Protein precipitation by methanol/chloroform method Basic Protocol 4: Trypsin digestion and proteomic analysis Alternate protocol 1: Pre-resin digestion and peptide level enrichment.

S-acylation, palmitoylation, plasma membrane prote↗
DOE OSTI2024

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

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

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Plug & play directed evolution of proteins with gradient-based discrete MCMC

Abstract A long-standing goal of machine-learning-based protein engineering is to accelerate the discovery of novel mutations that improve the function of a known protein. We introduce a sampling framework for evolving proteins in silico that supports mixing and matching a variety of unsupervised models, such as protein language models, and supervised models that predict protein function from sequence. By composing these models, we aim to improve our ability to evaluate unseen mutations and constrain search to regions of sequence space likely to contain functional proteins. Our framework achieves this without any model fine-tuning or re-training by constructing a product of experts distribution directly in discrete protein space. Instead of resorting to brute force search or random sampling, which is typical of classic directed evolution, we introduce a fast Markov chain Monte Carlo sampler that uses gradients to propose promising mutations. We conduct in silico directed evolution experiments on wide fitness landscapes and across a range of different pre-trained unsupervised models, including a 650 M parameter protein language model. Our results demonstrate an ability to efficiently discover variants with high evolutionary likelihood as well as estimated activity multiple mutations away from a wild type protein, suggesting our sampler provides a practical and effective new paradigm for machine-learning-based protein engineering.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Skeletides: A Modular, Simplified Physical Model of Protein Secondary Structure

Three-dimensional (3D) models are essential for visualization and conceptual understanding of complex architectures such as protein structure. Although there is a plethora of software platforms that allow digital depictions of protein structure at an atomic level in silico, physical models are needed to convey an intuitive understanding of biomolecular architecture. However, it is a challenge to represent all the relevant features of proteins in a single physical model due to their sheer structural complexity. Here, we describe a modular protein model that focuses only on representation of the secondary structure—the underlying structural skeleton. The simplified model consists of amino acid units, which can be linked together to reproduce the two most fundamental structural features of protein secondary structure: the relative positions of the amino acid alpha carbon atoms, and the intra-main chain hydrogen bonding pattern. We use 3D printing and magnets to create a set of three modular amino acid building blocks, which when linked together into a chain, can faithfully represent alpha helices, beta sheets, and turns. These simple to make models can be used to quickly assemble the alpha carbon trace of an entire protein domain, which conveys a tactile experience of the complexity of protein skeletal architecture. These models highlight the modularity of protein structure: where a single structural unit, the amino acid, can be linked together to form a larger, regular secondary structure. These models also have a propensity to spontaneously organize into alpha helices and beta sheets, as demonstrated by their ability to autonomously assemble when placed in a circulating water tank. These models provide a missing educational tool to expand knowledge of protein structure, foster deeper insight into protein folding, and inspire greater interest in biomacromolecular architecture.

36 MATERIALS SCIENCE↗