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At least 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

The temporal onset of associations of cortical proteins with cognitive resilience vary during late life

Background: Cortical proteins associated with cognitive resilience have been identified but their temporal onset in older adults is unknown. We present a multistage approach to first identify cortical proteins associated with cognitive resilience and then examine their associated temporal onset. Methods: We used data from a subset of 1088 decedents from two cohort-studies who had selected reaction monitoring proteomics from the dorsolateral prefrontal cortex, and at least 3 cognitive assessments. Cognition was assessed using a composite derived from 19 tests. We first used linear mixed-effects models to identify cortical proteins associated with cognitive resilience. We then used functional mixed-effects models to examine non-linear associations between proteins and cognitive resilience to identify their temporal onset. Results: Mean age at death was 90 years (SD = 6.4); 69 % were female. On average, cognition started to decline at around 15 years before death, with accelerated decline in the last 7 years. We identified 40 proteins associated with cognitive resilience, of which 17 proteins also showed non-linear associations. Non-linear associations indicated that higher levels of 10 proteins were associated with slower cognitive decline between 23 and 4 years before death. In contrast, higher levels of 7 proteins were associated with faster decline only within the last 7 years before death. Conclusions: Cognitive resilience proteins are differentially related to late-life cognitive aging; the onset of proteins that maintain cognition may begin many years before the onset of proteins that hasten cognitive decline. The temporal onset of cognitive resilience proteins may be crucial for timing efficacious interventions.

Zammit, Andrea↗
DOE OSTI2026

A curated benchmark for cofolding models on kinase conformational states

Abstract Protein kinases are critical drug targets, requiring therapeutics that can modulate their active and inactive conformational states. While cofolding models can generate global folds directly from kinase sequences and ligand SMILES strings, these models have not yet been tested on their ability to recover ligand-induced-fit conformational states of the kinase proteins. Here, we introduce KinConfBench, a curated benchmark of 2225 high-quality human kinase chains to evaluate the ability of four state-of-the-art cofolding models—Boltz-2, Chai-1, Protenix, and RoseTTAFold-All-Atom—to recover both canonical and rare conformational states. We show that geometric success metrics of a ligand pose in the active site do not correlate strongly with the correct kinase conformational state, motivating a new set of dynamical benchmarks for assessing cofolding models. While all four cofolding models achieve ~60–80% prediction accuracy for kinase conformational classification, they exhibit severe mode collapse when performing multiple inferences, show negligible structural diversity in sampling induced-fit motions, and display a prevalent “apo-drift” in which most cofolding models predominantly predict the kinase to be in its ligand-free state. Our results highlight that capturing ligand-induced protein conformational diversity, not just geometric fit, is critical for next-generation structure-based drug discovery.

Sun, Kunyang↗
NASA NTRS2000

A pollen-specific novel calmodulin-binding protein with tetratricopeptide repeats

Calcium is essential for pollen germination and pollen tube growth. A large body of information has established a link between elevation of cytosolic Ca(2+) at the pollen tube tip and its growth. Since the action of Ca(2+) is primarily mediated by Ca(2+)-binding proteins such as calmodulin (CaM), identification of CaM-binding proteins in pollen should provide insights into the mechanisms by which Ca(2+) regulates pollen germination and tube growth. In this study, a CaM-binding protein from maize pollen (maize pollen calmodulin-binding protein, MPCBP) was isolated in a protein-protein interaction-based screening using (35)S-labeled CaM as a probe. MPCBP has a molecular mass of about 72 kDa and contains three tetratricopeptide repeats (TPR) suggesting that it is a member of the TPR family of proteins. MPCBP protein shares a high sequence identity with two hypothetical TPR-containing proteins from Arabidopsis. Using gel overlay assays and CaM-Sepharose binding, we show that the bacterially expressed MPCBP binds to bovine CaM and three CaM isoforms from Arabidopsis in a Ca(2+)-dependent manner. To map the CaM-binding domain several truncated versions of the MPCBP were expressed in bacteria and tested for their ability to bind CaM. Based on these studies, the CaM-binding domain was mapped to an 18-amino acid stretch between the first and second TPR regions. Gel and fluorescence shift assays performed with CaM and a CaM-binding synthetic peptide further confirmed MPCBP binding to CaM. Western, Northern, and reverse transcriptase-polymerase chain reaction analysis have shown that MPCBP expression is specific to pollen. MPCBP was detected in both soluble and microsomal proteins. Immunoblots showed the presence of MPCBP in mature and germinating pollen. Pollen-specific expression of MPCBP, its CaM-binding properties, and the presence of TPR motifs suggest a role for this protein in Ca(2+)-regulated events during pollen germination and growth.

NASA Discipline Plant Biology↗
DOE OSTI2025

ProteinTuneRL

ProteinTuneRL is a framework designed to harness the power of reinforcement learning for advanced protein design. The project enables fine-tuning of generative models to explore and optimize protein sequences with tailored structural and functional properties.

Landajuela Larma, Mikel [Lawrence Livermore Nation↗
DOE OSTI2024

Evaluation of two inoculation routes of an adenovirus-mediated viral protein inhibitor in a Crimean-Congo hemorrhagic fever mouse model

Crimean-Congo hemorrhagic fever virus (CCHFV) is a tick-borne nairovirus with a wide geographic spread that can cause severe and lethal disease. No specific medical countermeasures are approved to combat this illness. The CCHFV L protein contains an ovarian tumor (OTU) domain with a cysteine protease thought to modulate cellular immune responses by removing ubiquitin and ISG15 post-translational modifications from host and viral proteins. Viral deubiquitinases like CCHFV OTU are attractive drug targets, as blocking their activity may enhance cellular immune responses to infection, and potentially inhibit viral replication itself. We previously demonstrated that the engineered ubiquitin variant CC4 is a potent inhibitor of CCHFV replication in vitro. A major challenge of the therapeutic use of small protein inhibitors such as CC4 is their requirement for intracellular delivery, e.g., by viral vectors. In this study, we examined the feasibility of in vivo CC4 delivery by a replication-deficient recombinant adenovirus (Ad-CC4) in a lethal CCHFV mouse model. Since the liver is a primary target of CCHFV infection, we aimed to optimize delivery to this organ by comparing intravenous (tail vein) and intraperitoneal injection of Ad-CC4. While tail vein injection is a traditional route for adenovirus delivery, in our hands intraperitoneal injection resulted in higher and more widespread levels of adenovirus genome in tissues, including, as intended, the liver. However, despite promising in vitro results, neither route of in vivo CC4 treatment resulted in protection from a lethal CCHFV infection.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS1999

A model of EcoRII restriction endonuclease action: the active complex is most likely formed by one protein subunit and one DNA recognition site

To elucidate the mechanism of interaction of restriction endonuclease EcoRII with DNA, we studied by native gel electrophoresis the binding of this endonuclease to a set of synthetic DNA-duplexes containing the modified or canonical recognition sequence 5'-d(CCA/TGG)-3'. All binding substrate or substrate analogues tested could be divided into two major groups: (i) duplexes that, at the interaction with endonuclease EcoRII, form two types of stable complexes on native gel in the absence of Mg2+ cofactor; (ii) duplexes that form only one type of complex, observed both in the presence and absence of Mg2+. Unlike the latter, duplexes under the first group can be hydrolyzed by endonuclease. Data obtained suggest that the active complex is most likely formed by one protein subunit and one DNA recognition sequence. A model of EcoRII endonuclease action is presented.

Oligodeoxyribonucleotides/chemistry/metabolism↗
NASA NTRS1987

Pharmacological activities in thermal proteins: relationships in molecular evolution

The model of protobiological events that has been presented in these pages has increasing relevance to pharmacological research. The thermal proteins that function as key substances in the proteinoid theory have recently been found to prolong the survival of rat forebrain neurons in culture and to stimulate the growth of neurites. A search for such activity in thermal proteins added to cultures of modern neurons was suggested by the fact that some of the microspheres assembled from proteinoids rich in hydrophobic amino acids themselves generate fibrous outgrowths.

NASA Discipline Exobiology↗
NASA NTRS2003

Growth and Metabolism of the Green Alga, Chlorella Pyrenoidosa, in Simulated Microgravity

The effect of microgravity on living organisms during space flight has been a topic of interest for some time, and a substantial body of knowledge on the subject has accumulated. Despite this, comparatively little information is available regarding the influence of microgravity on algae, even though it has been suggested for long duration flight or occupancy in space that plant growth systems, including both higher plants and algae, are likely to be necessary for bioregenerative life support systems. High-Aspect-Ratio Rotating-Wall Vessel or HARV bioreactors developed at Johnson Space Center provide a laboratory-based approach to investigating the effects of microgravity on cellular reactions. In this study, the HARV bioreactor was used to examine the influence of simulated microgravity on the growth and metabolism of the green alga, Chlorella pyrenoidosa. After the first 2 days of culture, cell numbers increased more slowly in simulated microgravity than in the HARV gravity control; after 7 days, growth in simulated microgravity was just over half (58%) that of the gravity control and at 14 days it was less than half (42%). Chlorophyll and protein were also followed as indices of cell competence and function; as with growth, after 2-3 days, protein and chlorophyll levels were reduced in modeled microgravity compared to gravity controls. Photosynthesis is a sensitive biochemical index of the fitness of photosynthetic organisms; thus, CO2-dependent O2 evolution was tested as a measure of photosynthetic capacity of cells grown in simulated microgravity. When data were expressed with respect to cell number, modeled microgravity appeared to have little effect on CO2 fixation. Thus, even though the overall growth rate was lower for cells cultured in microgravity, the photosynthetic capacity of the cells appears to be unaffected. Cells grown in simulated microgravity formed loose clumps or aggregates within about 2 days of culture, with aggregation increasing over time. Presently, the basis for, or significance of, the cell aggregation is unknown. The results from this study suggest that cell growth and morphological characteristics of green algae may be altered by culture in simulated microgravity. The data obtained to date should provide a solid basis for additional experimentation regarding the influence of modeled microgravity on cell morphology, physiological activity, protein production and possibly gene expression in algal and plant cell systems. The final aim of the study is to provide useful information to elucidate the underlying mechanism for the biological effects of microgravity on cells.

Mills, W. Ronald↗
NASA NTRS2006

Efficient Agent-Based Models for Non-Genomic Evolution

Modeling dynamical systems composed of aggregations of primitive proteins is critical to the field of astrobiological science involving early evolutionary structures and the origins of life. Unfortunately traditional non-multi-agent methods either require oversimplified models or are slow to converge to adequate solutions. This paper shows how to address these deficiencies by modeling the protein aggregations through a utility based multi-agent system. In this method each agent controls the properties of a set of proteins assigned to that agent. Some of these properties determine the dynamics of the system, such as the ability for some proteins to join or split other proteins, while additional properties determine the aggregation s fitness as a viable primitive cell. We show that over a wide range of starting conditions, there are mechanisins that allow protein aggregations to achieve high values of overall fitness. In addition through the use of agent-specific utilities that remain aligned with the overall global utility, we are able to reach these conclusions with 50 times fewer learning steps.

Gupta, Nachi↗
DOE OSTI2026

Spatiotemporal 4D Whole-cell Modeling of a Minimal Autotroph Reveals Central Carbon Metabolism Regulated Locally by Protein Megacomplexes via Post-translational Modifications under Light Disturbance

Photosynthetic microorganisms rely on multiple pathways in central carbon metabolism to adapt to fluctuating light and energy availability across diel cycles. Mechanistic insight into the regulatory dynamics of this adaptation requires integrating processes spanning disparate timescales, from rapid redox-dependent post-translational modifications (PTMs) to slower changes in protein expression and metabolic pathway usage. To address this complexity beyond genome-based inference and traditional modeling, we develop a whole-cell four-dimensional (3D + time) model of the marine cyanobacterium Prochlorococcus marinus MED4 that explicitly represents the spatial organization of enzymatic and molecular processes in central carbon metabolism under light perturbation. We employ a perturbation-based research design to experimentally generate time-series, multi-omics measurements that provide molecular descriptors and cryo-ET derived 3D segmented volumes as constraints for this dynamic 4D framework. The integration of experiments and modeling across defined light regimes enables quantitative validation of system-level responses and forecasting under distinct light disturbances. We test the hypothesis that light-dependent redox PTMs regulating the structural assembly of a protein megacomplex, the “dark complex,” modulate metabolic flux at a conserved regulatory node of the Calvin–Benson cycle (CBC) in cyanobacteria. Our model shows that subcellular spatial organization buffers rapid light-induced changes in thylakoid reaction rates, which are followed by redox-PTM-mediated sequestration or release of CBC enzymes in the dark complex, ultimately impacting carbon fixation dynamics within carboxysomes. Comparison with an equivalently parameterized well-mixed stochastic model demonstrates that post-translational regulation not only buffers transcriptional noise and diffusion-driven fluctuations but also stabilizes phenotypic outcomes, underscoring the importance of spatial heterogeneity in phenotypic robustness. This ability to probe adaptive, spatiotemporally resolved mechanisms in photosynthetic machinery and central carbon metabolism addresses a critical gap in genotype-to-phenotype inference and expands modeling and design capabilities for understudied or genetically intractable autotrophs such as P. marinus MED4.

Johnson, Connah G.↗
NASA NTRS1999

Molecular Mechanisms Regulating Muscle Fiber Composition Under Microgravity

The overall goal of this project is to reveal the molecular mechanisms underlying the selective and debilitating atrophy of specific skeletal muscle fiber types that accompanies sustained conditions of microgravity. Since little is currently known about the regulation of fiber-specific gene expression programs in mammalian muscle, elucidation of the basic mechanisms of fiber diversification is a necessary prerequisite to the generation of therapeutic strategies for attenuation of muscle atrophy on earth or in space. Vertebrate skeletal muscle development involves the fusion of undifferentiated mononucleated myoblasts to form multinucleated myofibers, with a concomitant activation of muscle-specific genes encoding proteins that form the force-generating contractile apparatus. The regulatory circuitry controlling skeletal muscle gene expression has been well studied in a number of vertebrate animal systems. The goal of this project has been to achieve a similar level of understanding of the mechanisms underlying the further specification of muscles into different fiber types, and the role played by innervation and physical activity in the maintenance and adaptation of different fiber phenotypes into adulthood. Our recent research on the genetic basis of fiber specificity has focused on the emergence of mature fiber types and have implicated a group of transcriptional regulatory proteins, known as E proteins, in the control of fiber specificity. The restriction of E proteins to selected muscle fiber types is an attractive hypothetical mechanism for the generation of muscle fiber-specific patterns of gene expression. To date our results support a model wherein different E proteins are selectively expressed in muscle cells to determine fiber-restricted gene expression. These studies are a first step to define the molecular mechanisms responsible for the shifts in fiber type under conditions of microgravity, and to determine the potential importance of E proteins as upstream targets for the effects of weightlessness. In the past year we have determined that the expression of E Proteins is restricted to specific fiber types by post-transcriptional mechanisms. By far, the most prevalent mechanism of cellular control for achieving post-transcriptional regulation of gene expression is selective proteolysis -through the ubiquitin -proteasome pathway. Steady-state levels of HEB message are similar in all fast and slow skeletal muscle fiber types, yet the protein is restricted to Type IIX fibers. HEB appears to be a nodal point for regulating fiber-specific transcription, as expression of the transcription factor is regulated at the post-transcriptional level. It is not clear at present whether the regulation is at the level of protein synthesis or degradation. We are now poised to evaluate the biological role of ubiquitination in fiber specific-gene expression by controlling the post-transcriptional expression of E Proteins. The use of metabolic labelling and pharmacological inhibitors of the ubiquitin pathway will be used to identify the mode of regulation of the Type IIX expression pattern. The potential role of specific kinases in effecting the restriction of HEB expression will be examined by using both inhibitors and activators. The results of these studies will provide the necessary information to evaluate the biological role of E proteins in controlling fiber type transitions, and in potentially attenuating the atrophic effects of microgravity conditions. We have also recently shown that ectopic expression of the HEB protein transactivates the Type IIX-specific skeletal a-actin reporter. The 218 bp skeletal a-actin promoter drives transgene expression solely in mature Type IIX fibers. A mouse also carrying the transgene MLCI/HEB (which ectopically expresses the E Protein HEB in Type IIB fibers) forces expression of the skeletal a-actin reporter gene in Type IIB fibers. We can now dissect the composition of this fiber-specific cis-element. The skeletal a-actin promoter is quite compact and has been extensively characterized in vitro for activity and binding factors. The single E box may act as a binding target of myogenic factor/HEB heterodimer to allow for IIX expression. The HEB transcription factor may recognize either the precise flanking sequences of the E Box, or perhaps interacting with other proteins bound nearby, and activating expression in Type IIX fibers. This E box will be both ablated, and alternatively, as ablation may well destroy any muscle-specific transcriptional activity, flanking sequences substituted with those surrounding the E box (El) of the myogenin promoter. Modification of fiber-specific transgene expression will be tested in transgenic mice. The results of these studies will provide basic information on the regulatory circuitry underlying fiber specificity, and will form the basis for building appropriate transgenic regulatory cassettes to effect fiber transitions in subsequent experimental manipulations on unweighted muscles.

Rosenthal, Nadia A.↗
DOE OSTI2025

Allosteric prediction via convolutional neural networks and protein structural and dynamical features

Allostery is the phenomenon whereby a binding event or covalent modification at one site in a protein modulates function at a distal site, thus changing a protein’s functional state. As such, it is a ubiquitous aspect of protein functional regulation. Computationally predicting allosteric states is important as part of the broader challenge of functional annotation, but it also has practical implications for drug development, as targeting an allosteric site often affords greater specificity compared with targeting an orthosteric site. This study introduces a machine learning approach to predict the allosteric functional state using the small G-protein KRas as the model system, due to its implication in many types of cancer and being well studied as a result with many x-ray crystallographic structures of KRas available with different mutations and ligands bound. Using structural and dynamical features that can be cast as images, namely interatomic distances, contact maps, covariance, and mutual information, supervised learning was performed using convolutional neural networks. Two pretrained convolutional neural network architectures, GoogLeNet and ResNet18, were fine-tuned to classify KRas into active or inactive states based on these features. Across training regimes, atomic contact maps emerged as the most effective structural feature, whereas linearized mutual information outperformed covariance in capturing dynamical correlations relevant to allostery. Models achieved significant validation accuracy, with atomic contact maps yielding up to 90% accuracy. In conclusion, the findings suggest that integrating global structural rearrangements and correlated motion patterns with deep learning can reliably predict protein allosteric states, offering a promising framework for understanding allosteric regulation and developing targeted therapeutics.

Rajeshwar T., Rajitha [Oak Ridge National Laborato↗
NASA NTRS2003

A constitutively activated mutant of human soluble guanylyl cyclase (sGC): implication for the mechanism of sGC activation

Heterodimeric alphabeta soluble guanylyl cyclase (sGC) is a recognized receptor for nitric oxide (NO) and mediates many of its physiological functions. Although it has been clear that the heme moiety coordinated by His-105 of the beta subunit is crucial for mediating the activation of the enzyme by NO, it is not understood whether the heme moiety plays any role in the function of the enzyme in the absence of NO. Here we analyze the effects of biochemical and genetic removal of heme and its reconstitution on the activity of the enzyme. Detergent-induced loss of heme from the wild-type alphabeta enzyme resulted in several-fold activation of the enzyme. This activation was inhibited after hemin reconstitution. A heme-deficient mutant alphabetaCys-105 with Cys substituted for His-105 was constitutively active with specific activity approaching the activity of the wild-type enzyme activated by NO. However, reconstitution of mutant enzyme with heme and/or DTT treatment significantly inhibited the enzyme. Mutant enzyme reconstituted with ferrous heme was activated by NO and CO alone and showed additive effects between gaseous effectors and the allosteric activator 5-cyclopropyl-2-[1-(2-fluoro-benzyl)-1H-pyrazolo[3,4-b]pyridin-3-yl]-pyrim idin-4-ylamine. We propose that the heme moiety through its coordination with His-105 of the beta subunit acts as an endogenous inhibitor of sGC. Disruption of the heme-coordinating bond induced by binding of NO releases the restrictions imposed by this bond and allows the formation of an optimally organized catalytic center in the heterodimer.

Non-NASA Center↗
NASA NTRS2004

Unfolding of Proteins: Thermal and Mechanical Unfolding

We have employed a Hamiltonian model based on a self-consistent Gaussian appoximation to examine the unfolding process of proteins in external - both mechanical and thermal - force elds. The motivation was to investigate the unfolding pathways of proteins by including only the essence of the important interactions of the native-state topology. Furthermore, if such a model can indeed correctly predict the physics of protein unfolding, it can complement more computationally expensive simulations and theoretical work. The self-consistent Gaussian approximation by Micheletti et al. has been incorporated in our model to make the model mathematically tractable by signi cantly reducing the computational cost. All thermodynamic properties and pair contact probabilities are calculated by simply evaluating the values of a series of Incomplete Gamma functions in an iterative manner. We have compared our results to previous molecular dynamics simulation and experimental data for the mechanical unfolding of the giant muscle protein Titin (1TIT). Our model, especially in light of its simplicity and excellent agreement with experiment and simulation, demonstrates the basic physical elements necessary to capture the mechanism of protein unfolding in an external force field.

Hur, Joe S.↗
NASA NTRS1999

Collagenase-3 binds to a specific receptor and requires the low density lipoprotein receptor-related protein for internalization

We have previously identified a specific receptor for collagenase-3 that mediates the binding, internalization, and degradation of this ligand in UMR 106-01 rat osteoblastic osteosarcoma cells. In the present study, we show that collagenase-3 binding is calcium-dependent and occurs in a variety of cell types, including osteoblastic and fibroblastic cells. We also present evidence supporting a two-step mechanism of collagenase-3 binding and internalization involving both a specific collagenase-3 receptor and the low density lipoprotein receptor-related protein. Ligand blot analysis shows that (125)I-collagenase-3 binds specifically to two proteins ( approximately 170 kDa and approximately 600 kDa) present in UMR 106-01 cells. Western blotting identified the 600-kDa protein as the low density lipoprotein receptor-related protein. Our data suggest that the 170-kDa protein is a specific collagenase-3 receptor. Low density lipoprotein receptor-related protein-null mouse embryo fibroblasts bind but fail to internalize collagenase-3, whereas UMR 106-01 and wild-type mouse embryo fibroblasts bind and internalize collagenase-3. Internalization, but not binding, is inhibited by the 39-kDa receptor-associated protein. We conclude that the internalization of collagenase-3 requires the participation of the low density lipoprotein receptor-related protein and propose a model in which the cell surface interaction of this ligand requires a sequential contribution from two receptors, with the collagenase-3 receptor acting as a high affinity primary binding site and the low density lipoprotein receptor-related protein mediating internalization.

NASA Discipline Musculoskeletal↗
NASA NTRS2001

Modest increased sensitivity to radiation oncogenesis in ATM heterozygous versus wild-type mammalian cells

Subpopulations that are genetically predisposed to radiation-induced cancer could have significant public health consequences. Individuals homozygous for null mutations at the ataxia telangiectasia gene are indeed highly radiosensitive, but their numbers are very small. Ataxia Telangiectasia heterozygotes (1-2% of the population) have been associated with somewhat increased radiosensitivity for some end points, but none directly related to carcinogenesis. Here, intralitter comparisons between wild-type mouse embryo fibroblasts and mouse embryo fibroblasts carrying ataxia telangiectasia mutated (ATM) null mutation indicate that the heterozygous cells are more sensitive to radiation oncogenesis than their normal, litter-matched, counterparts. From these data we suggest that Ataxia Telangiectasia heterozygotes could indeed represent a societally-significant radiosensitive human subpopulation.

Non-NASA Center↗
DOE OSTI2026

Energy-Screened Many-Body Expansion for Protein–Ligand Interactions: Examining Convergence for Metalloenzymes Through Seven–Body Interactions

Fragment-based quantum chemistry is a powerful strategy for calculating protein−ligand interaction energies using quantum chemistry methods. Rigorous convergence often requires hundreds of atoms in the protein binding-site model, especially if that model is constructed using distance-based criteria to select amino acid residues, while three- and four-body calculations exhibit instability related to combinatorial proliferation in the number of subsystem calculations. Here, we report an energy-based screening protocol for the many-body expansion applied to protein−ligand interactions, implemented in the open-source FRAGME∩T code. Using a combination of aggressive screening based on semiempirical quantum chemistry, with an improved graph-theoretical algorithm to eliminate unimportant subsystems, we are able to perform n-body calculations up to n = 7 using density functional theory in triple-ζ basis sets. Distance cutoffs further reduce the cost without compromising accuracy. Rapid and stable convergence of the many-body expansion is obtained by n = 4, for a pair of metalloenzymes in which a divalent ion coordinates directly to the ligand. As compared to previous results that relied solely on distance cutoffs, oscillations in the n-body corrections are reduced or eliminated, although residual errors remain in one case. This work demonstrates that benchmark-quality protein−ligand interaction energies can be systematically converged using a method with excellent parallel efficiency and scalability.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

From sequence to protein structure and conformational dynamics with artificial intelligence/machine learning

The 2024 Nobel Prize in Chemistry was awarded in part for de novo protein structure prediction using AlphaFold2, an artificial intelligence/machine learning (AI/ML) model trained on vast amounts of sequence and three-dimensional structure data. AlphaFold2 and related models, including RoseTTAFold and ESMFold, employ specialized neural network architectures driven by attention mechanisms to infer relationships between sequence and structure. At a fundamental level, these AI/ML models operate on the long-standing hypothesis that the structure of a protein is determined by its amino acid sequence. More recently, AlphaFold2 has been adapted for the prediction of multiple protein conformations by subsampling multiple sequence alignments. Herein, we provide an overview of the deterministic relationship between sequence and structure, which was hypothesized over half a century ago with profound implications for the biological sciences ever since. We postulate that protein conformational dynamics are also determined, at least in part, by amino acid sequence and that this relationship may be leveraged for construction of AI/ML models dedicated to predicting protein conformational ensembles. Accordingly, we describe a conceptual model architecture, which may be trained on sequence data in combination with conformationally sensitive structural information, coming primarily from nuclear magnetic resonance (NMR) spectroscopy. Notwithstanding certain limitations in this context, NMR offers abundant structural heterogeneity conducive to conformational ensemble prediction. As NMR and other data continue to accumulate, sequence-informed prediction of protein structural dynamics with AI/ML has the potential to emerge as a transformative capability across the biological sciences.

Artificial intelligence↗