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DOE OSTI2025

CAML: Commutative Algebra Machine Learning─A Case Study on Protein–Ligand Binding Affinity Prediction

Recently, Suwayyid and Wei introduced commutative algebra as an emerging paradigm for machine learning and data science. In this work, we propose commutative algebra machine learning (CAML) for the prediction of protein−ligand binding affinities. Specifically, we apply persistent Stanley−Reisner theory, a key concept in combinatorial commutative algebra, to the affinity predictions of protein−ligand binding and metalloprotein−ligand binding. We present three new algorithms, i.e., element-specific commutative algebra, category-specific commutative algebra, and commutative algebra on bipartite complexes, to tackle the complexity of data involved in (metallo) protein−ligand complexes. We show that the proposed CAML outperforms other state-of-theart methods in (metallo) protein−ligand binding affinity predictions, indicating the great potential of commutative algebra learning.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

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↗

DOE OSTI2025

Control-Affine Schrödinger Bridge and Generalized Bohm Potential

From a stochastic control perspective, the Schrödinger bridge is a density-valued continuous curve parameterized by time that connects a given pair of initial and terminal probability densities via minimum effort controlled Brownian motion. The control-affine Schrödinger bridge extends this idea to a generic control-affine Itô diffusion, possibly with an additive state cost. Here, in this letter, we recast the necessary conditions of optimality for the control-affine Schrödinger bridge problem as a two point boundary value problem for a quantum mechanical Schrödinger PDE with complex potential. This complex-valued potential is a generalization of the real-valued Bohm potential in quantum mechanics. Our derived potential is akin to the optical potential in nuclear physics where the real part of the potential encodes elastic scattering (transmission of wave function), and the imaginary part encodes inelastic scattering (absorption of wave function). The key takeaway is that the process noise that drives the evolution of probability densities induces an absorbing medium in the evolution of wave function. These results make new connections between control theory and non-equilibrium statistical mechanics through the lens of quantum mechanics.

Markov processes↗

DOE OSTI2020

The emergence of small-scale self-affine surface roughness from deformation

Most natural and man-made surfaces appear to be rough on many length scales. There is presently no unifying theory of the origin of roughness or the self-affine nature of surface topography. One likely contributor to the formation of roughness is deformation, which underlies many processes that shape surfaces such as machining, fracture, and wear. Using molecular dynamics, we simulate the biaxial compression of single-crystal Au, the high-entropy alloy Ni 36.67 Co 30 Fe 16.67 Ti 16.67 , and amorphous Cu 50 Zr 50 and show that even surfaces of homogeneous materials develop a self-affine structure. By characterizing subsurface deformation, we connect the self-affinity of the surface to the spatial correlation of deformation events occurring within the bulk and present scaling relations for the evolution of roughness with strain. These results open routes toward interpreting and engineering roughness profiles.

42 ENGINEERING↗

DOE OSTI2024

Exploring Architectural-Aware Affinity Policies in Modern HPC Runtimes

Modern commodity and High-Performance Computing (HPC) systems are evolving with complex CPU architectures. These architectures now feature higher core and NUMA domain counts and implement features such as hyperthreading. When considering significant differences in hardware configurations, library availability, and hardware-tailored system/software stacks, which could substantially vary from one system to another, performance portability is hard to achieve. Throughout the years, this trend resulted in an increasingly high burden on application developers to fine-tune their workloads for each architecture. This work explores how hardware-dependent aspects such as locality/process/thread affinity affect performance in modern CPU architectures. We focus our study on the Global Memory and Threading (GMT) distributed runtime system as a representative of Partitioned Global Address Space (PGAS) software stacks commonly adopted for productivity. In particular, to appreciate performance implications, we evaluate GMT’s thread affinity policies, and, introduce two new ones which exploit architectural awareness. Finally, we explore alternative NUMA configurations via different process bindings and perform a scalability study on three HPC clusters with varying CPU architectures and NUMA layouts. Our analysis indicates that more complex architectures are more affected by affinity and binding policies and highlights the importance of setting proper runtime configurations to achieve superior performance.

Di Dio Lavore, Ian↗

DOE OSTI2022

Reversible domain closure modulates GlnBP ligand binding affinity

Glutamine binding protein (GlnBP) is an Escherichia Coli periplasmic binding protein, which binds and carries glutamine to the inner membrane ATP-binding cassette (ABC) transporter. GlnBP binds the ligand with affinity around 0.1μM measured by isothermal titration calorimetry (ITC) and ligand binding stabilizes protein structure shown by its increase in thermodynamic stability. However, the molecular determinant of GlnBP ligand binding is not known. Electrostatic and hydrophobic interaction between GlnBP and glutamine are critical factors. We propose that the freedome of closure movement is also vital for ligand binding. In order to approve this hypothesis, we generate a series of mutants with different linker length that has different magnitude of domain closure. Mutants show different ligand binding affinity, which indicates that the propensity of domain closure determines the ligand binding affinity. Ligand binding triggers gradual ensemble conformational change. Structural changes upon ligand binding are monitored by combination of small angle x-ray scattering (SAXS) and NMR spectroscopy. Detailed structure characterization of GlnBP contributes to a better understanding of ligand binding and provides the structural basis for biosensor design.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2026

Directed evolution of a stem-helix–targeting antibody enables MERS-CoV cross-neutralization through enhanced binding affinity

Broadly neutralizing antibodies (bnAbs) targeting conserved regions of the betacoronavirus spike are important for pan-betacoronavirus protection and pandemic preparedness. Here, we report the isolation of a human monoclonal antibody, CC65.1, from a SARS-CoV-2 convalescent donor that targets the conserved S2 stem helix region. CC65.1 neutralizes various sarbecoviruses, including SARS-CoV-2, and binds to the MERS-CoV spike but lacks MERS-CoV-neutralizing activity due to insufficient binding affinity. We utilized directed evolution to enhance the binding affinity of CC65.1 for the MERS-CoV S2 stem helix, yielding engineered antibody variants with newly acquired MERS-CoV-neutralizing activity. High-resolution structural analysis reveals key paratope mutations that enhance binding and stabilize epitope engagement. Our findings demonstrate the potential of in vitro affinity maturation to expand the neutralization breadth of stem-helix-targeting antibodies across divergent betacoronaviruses. This work supports the development of engineered bnAbs for broadly protective betacoronavirus countermeasures and provides a strategy for achieving cross-lineage neutralization.

Zhou, Panpan↗

DOE OSTI2021

In vitro evolution-enabled selection of affinity reagents against bio/chem-threats [Slides]

Affinity reagent are molecules whose function depends on specific recognition of a target. The function can allow therapeutic and/or diagnostic applications. The support CBDP pillars are--Understand: high accuracy/sensitivity, low cost, deployable detection; Protect: pre-event treatment of soldiers; and Mitigate: post-event treatment of soldiers; help identifying the need for specific decontamination protocols. The Los Alamos affinity reagents team (LA-ART) can can obtain affinity reagents to any target, on demand, using a well-established in vitro evolution platform. Recent success stories are shared.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗

NASA NTRS1986

On the electron affinity of the oxygen atom

The electron affinity (EA) of oxygen is computed to be 1.287 eV, using 2p electron full configuration-interaction (CI) wave functions expanded in a 6s5p3d2f Slater-type orbital basis. The best complete active space self-consistent field - multireference CI (CASSCF-MRCI) result including only 2p correlation is 1.263 eV. However, inclusion of 2s intrashell and 2s2p intershell correlation increases the computed EA to 1.290 at the CASSCF-MRCI level. At the full CI basis set limit, the 2s contribution to the electron affinity is estimated to be as large as 0.1 eV. This study clearly establishes the synergistic effect between the higher excitations and basis set completeness on the electron affinity when the 2s electrons are correlated.

Bauschlicher, C. W., Jr.↗

NASA NTRS1986

On the electron affinity of the oxygen atom

The electron affinity of oxygen is computed to be 1.287 eV, at the full CI level using a 6s5p3d 2f Slater-type orbital basis and correlating only the 2p electrons. The best CASSCF-MRCI result including only 2p correlation is 1.263 eV. However, inclusion of 2s intrashell and 2s2p intershell coorelation increases the computed EA to 1.290 eV at the CASSCF-MRCI level. At the full CI basis set limit, the 2s contribution to the electron affinity is estimated to be as large as 0.1 eV. The higher excitation contribution to the electron affinity is found to increase substantially with basis set completeness, especially when the 2s electrons are correlated. Relativistic effects are shown to make a small (less than 0.01 eV) change in the EA.

Bauschlicher, C. W., Jr.↗

NASA NTRS1996

Optimized Structures and Proton Affinities of Fluorinated Dimethyl Ethers: An Ab Initio Study

Ab initio methods have been used to investigate the proton affinity and the geometry changes upon protonation for the molecules (CH3)2O, (CH2F)2O, (CHF2)2O, and (CF3)2O. Geometry optimizations were performed at the MP2/3-2 I G level, and the resulting geometries were used for single-point energy MP2/6-31G calculations. The proton affinity calculated for (CH3)2O was 7 Kjoule/mole from the experimental value, within the desired variance of +/- 8Kjoule/mole for G2 theory, suggesting that the methodology used in this study is adequate for energy difference considerations. For (CF3)20, the calculated proton affinity of 602 Kjoule/mole suggests that perfluorinated ether molecules do not act as Lewis bases under normal circumstances; e.g. degradation of commercial lubricants in tribological applications.

Orgel, Victoria B.↗

NASA NTRS1998

Theoretical and Experimental Determination of the Proton Affinity of (CF3CH2)2O

We report the experimental determination of the proton affinity of the molecule (CF3CH2)2O using chemical ionization mass spectrometry, and we compare it to the theoretical value obtained for protonation at the oxygen atom using the calculational methodology (MP2/6-31G**//MP2/3-21G). The proton affinity for this molecule as measured by bracketing experiments was between 724 kJ/mole and 741 kJ/mole. Ab initio (MP2/6-31G**//MP2/3-21G) calculations yield a value of about 729 kJ/mole, in agreement with the chemical ionization experiments. The results of these and related calculations suggest that the (MP2/6-31G**//MP2/3-21G) methodology is acceptable for estimating the proton affinities of partially-and fully-fluorinated methyl and ethyl ethers. We submit that any conclusions about the chemistry of fluoroether polymer lubricants based on their basicity can also be predicted reliably with such calculations.

Zehe, Michael J.↗

DOE OSTI2021

Using diverse potentials and scoring functions for the development of improved machine-learned models for protein–ligand affinity and docking pose prediction

The advent of computational drug discovery holds the promise of significantly reducing the effort of experimentalists, along with monetary cost. More generally, predicting the binding of small organic molecules to biological macromolecules has far-reaching implications for a range of problems, including metabolomics. However, problems such as predicting the bound structure of a protein–ligand complex along with its affinity have proven to be an enormous challenge. In recent years, machine learning-based methods have proven to be more accurate than older methods, many based on simple linear regression. Nonetheless, there remains room for improvement, as these methods are often trained on a small set of features, with a single functional form for any given physical effect, and often with little mention of the rationale behind choosing one functional form over another. Moreover, it is not entirely clear why one machine learning method is favored over another. Here, we endeavor to undertake a comprehensive effort towards developing high-accuracy, machine-learned scoring functions, systematically investigating the effects of machine learning method and choice of features, and, when possible, providing insights into the relevant physics using methods that assess feature importance. Here, we show synergism among disparate features, yielding adjusted R 2 with experimental binding affinities of up to 0.871 on an independent test set and enrichment for native bound structures of up to 0.913. When purely physical terms that model enthalpic and entropic effects are used in the training, we use feature importance assessments to probe the relevant physics and hopefully guide future investigators working on this and other computational chemistry problems.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2020

Affine Approximation of Parametrized Kernels and Model Order Reduction for Nonlocal and Fractional Laplace Models

In this work, we consider parametrized problems driven by spatially nonlocal integral operators with parameter-dependent kernels. In particular, kernels with varying nonlocal interaction radius $\delta > 0$ and fractional Laplace kernels, parametrized by the fractional power $s\in(0,1)$, are studied. Furthermore, in order to provide an efficient and reliable approximation of the solution for different values of the parameters, we develop the reduced basis method as a parametric model order reduction approach. Major difficulties arise since the kernels are not affine in the parameters, singular, and discontinuous. Moreover, the spatial regularity of the solutions depends on the varying fractional power $s$. To address this, we derive regularity and differentiability results with respect to $\delta$ and $s$, which are of independent interest for other applications such as optimization and parameter identification. We then use these results to construct affine approximations of the kernels by local polynomials. Finally, we certify the method by providing reliable a posteriori error estimators, which account for all approximation errors, and support the theoretical findings by numerical experiments.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS2002

Expression and affinity purification of recombinant proteins from plants

With recent advances in plant biotechnology, transgenic plants have been targeted as an inexpensive means for the mass production of proteins for biopharmaceutical and industrial uses. However, the current plant purification techniques lack a generally applicable, economic, large-scale strategy. In this study, we demonstrate the purification of a model protein, beta-glucuronidase (GUS), by employing the protein calmodulin (CaM) as an affinity tag. In the proposed system, CaM is fused to GUS. In the presence of calcium, the calmodulin fusion protein binds specifically to a phenothiazine-modified surface of an affinity column. When calcium is removed with a complexing agent, e.g., EDTA, calmodulin undergoes a conformational change allowing the dissociation of the calmodulin-phenothiazine complex and, therefore, permitting the elution of the GUS-CaM fusion protein. The advantages of this approach are the fast, efficient, and economical isolation of the target protein under mild elution conditions, thus preserving the activity of the target protein. Two types of transformation methods were used in this study, namely, the Agrobacterium-mediated system and the viral-vector-mediated transformation system. Copyright 2002 Elsevier Science (USA).

Non-NASA Center↗

NASA NTRS1999

Boronate affinity adsorption of RNA: possible role of conformational changes

Batch equilibrium adsorption isotherm determination is used to characterize the adsorption of mixed yeast RNA on agarose-immobilized m-aminophenylboronic acid. It is shown that the affinity-enhancing influence of divalent cations depends strongly on the precise nature of the cation used, with barium being far more effective than the conventionally-used magnesium. This adsorption-promoting influence of barium is suggested to arise primarily from ionic influences on the structure and rigidity of the RNA molecule, as the adsorption of ribose-based small molecules is not similarly affected. The substitution of barium for the standard magnesium counterion does not greatly promote the adsorption of DNA, implying that the effect is specific to RNA and may be useful in boronate-based RNA separations. RNA adsorption isotherms exhibit sharp transitions as functions of temperature, and these transitions occur at different temperatures with Mg2+ and with Ba2+. Adsorption affinity and capacity were found to increase markedly at lower temperatures, suggestive of an enthalpically favored interaction process. The stoichiometric displacement parameter, Z, in Ba2+ buffer is three times the value in Mg2+ buffer, and is close to unity.

NASA Discipline Environmental Health↗

DOE OSTI2022

Quantifying the anion effect of gas solubility within ionic liquids using the solvation affinity index

The solubility of gas molecules within ionic liquids (ILs) is strongly dictated by the anion effect and the free volume effect, and of these two influences, the anion effect can be challenging to predict. We evaluate a wide range of different gas solutes (O 2 , NH 3 , CH 4 , H 2 S, SO 2 , Ar, H 2 , N 2 , CO 2 ) in different ILs, and we find that the solubility is strongly correlated to one of three different IL properties, which can be directly calculated from the anion electrostatic potential (ESP) surfaces. Thus, the solutes can be grouped into three fundamental types, leading to a general descriptor, the solvation affinity index (SAI), which can identify the affinity between a wide range of gas solutes and IL solvents. The SAI of nine common industrial gases with 47 common anions are generated. Furthermore, this can provide a fast approach for pre-screening IL solvents or even for ILs embedded within porous supports.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2020

Binding Affinity Prediction by Pairwise Function Based on Neural Network

In this paper, we present a new approach to estimate the binding affinity from given three-dimensional poses of protein–ligand complexes. In this scheme, every protein–ligand atom pair makes an additive free-energy contribution. The sum of these pairwise contributions then gives the total binding free energy or the logarithm of the dissociation constant. The pairwise contribution is calculated by a function implemented via a neural network that takes the properties of the two atoms and their distance as input. The pairwise function is trained using a portion of the PDBbind 2018 data set. The model achieves good accuracy for affinity predictions when evaluated with PDBbind 2018 and with the CASF-2016 benchmark, comparing favorably to many scoring functions such as that of AutoDock Vina. The framework here may be extended to incorporate other factors to further improve its accuracy and power.

59 BASIC BIOLOGICAL SCIENCES↗