Estimation Of Affinity Research Articles

Bridging Bayesian and Minimax Mean Square Error Estimation via Wasserstein Distributionally Robust Optimization

We introduce a distributionally robust minimium mean square error estimation model with a Wasserstein ambiguity set to recover an unknown signal from a noisy observation. The proposed model can be viewed as a zero-sum game between a statistician choosing an estimator—that is, a measurable function of the observation—and a fictitious adversary choosing a prior—that is, a pair of signal and noise distributions ranging over independent Wasserstein balls—with the goal to minimize and maximize the expected squared estimation error, respectively. We show that, if the Wasserstein balls are centered at normal distributions, then the zero-sum game admits a Nash equilibrium, by which the players’ optimal strategies are given by an affine estimator and a normal prior, respectively. We further prove that this Nash equilibrium can be computed by solving a tractable convex program. Finally, we develop a Frank–Wolfe algorithm that can solve this convex program orders of magnitude faster than state-of-the-art general-purpose solvers. We show that this algorithm enjoys a linear convergence rate and that its direction-finding subproblems can be solved in quasi-closed form. Funding: This research was supported by the Swiss National Science Foundation [Grants BSCGI0_ 157733 and 51NF40_180545], an Early Postdoc.Mobility Fellowship [Grant P2ELP2_195149], and the European Research Council [Grant TRUST-949796].

Affine Arithmetic-Based Reliable Estimation of Transition State Boundaries for Uncertain Markov Chains

Markov Chain-based models have been extensively used in numerous power system applications, ranging from decision support systems, to reliability and resilience analysis. The state transition probabilities, which determine the overall model dynamics, have been traditionally described by deterministic values. This common assumption might not be suitable for analyzing modern power systems, where large number of complex and correlated uncertainties could significantly affect the correct evaluation and estimation of both model parameters and model outputs. In addressing that problem, this paper advocates the role of affine arithmetic, by proposing a reliable framework for Markov Chain transient analysis in the presence of data uncertainties. The main idea is to represent the uncertainties in state transition probabilities through affine forms, which then allow to apply affine arithmetic-based operators and to identify the corresponding transient state probabilities. Two case studies are presented and discussed, in order to demonstrate the effectiveness of the proposed methodology in practical power system problems and applications.

Being Positive is Not Everything - Experimental and Computational Studies on the Selectivity of a Self-Assembled, Multiple Redox-State Receptor that Binds Anions with up to Picomolar Affinities.

The interaction of the self-assembled trinuclear ruthenium bowl 13+ , that displays three other accessible oxidation states, with oxo-anions is investigated. Using a combination of NMR and electrochemical experimental data, estimates of the binding affinities of 14+ , 15+ , and 16+ for both halide and oxo-anions were derived. This analysis revealed that, across the range of oxidation states of the host, both high anion binding affinities (>109 M-1 for specific guests bound to 16+ ) and high selectivities (a range of >107 M-1 ) were observed. As the crystal structure of binding of the hexafluorophosphate anion revealed that the host has two potential binding sites (named the α and β pockets), the host-guest properties of both putative binding sites of the bowl, in all of its four oxidation states, were investigated through detailed quantum-based computational studies. These studies revealed that, due to the interplay of ion-ion interactions, charge-assisted hydrogen-bonding and anion-π interactions, binding to the α pocket is generally preferred, except for the case of the relatively large and lipophilic hexafluorophosphate anionic guest and the host in the highest oxidation states, where the β pocket becomes relatively favourable. This analysis confirms that host-guest interactions involving structurally complex supramolecular architectures are driven by a combination of non-covalent interactions and, even in the case of charged binding pairs, simple ion-ion interactions alone cannot accurately define these recognition processes.

Unsupervised random forest for affinity estimation

This paper presents an unsupervised clustering random-forest-based metric for affinity estimation in large and high-dimensional data. The criterion used for node splitting during forest construction can handle rank-deficiency when measuring cluster compactness. The binary forest-based metric is extended to continuous metrics by exploiting both the common traversal path and the smallest shared parent node.The proposed forest-based metric efficiently estimates affinity by passing down data pairs in the forest using a limited number of decision trees. A pseudo-leaf-splitting (PLS) algorithm is introduced to account for spatial relationships, which regularizes affinity measures and overcomes inconsistent leaf assign-ments. The random-forest-based metric with PLS facilitates the establishment of consistent and point-wise correspondences. The proposed method has been applied to automatic phrase recognition using color and depth videos and point-wise correspondence. Extensive experiments demonstrate the effectiveness of the proposed method in affinity estimation in a comparison with the state-of-the-art.

Testing the Homogeneity of “White”

The purpose of estimating each part of the biological profile is to reduce the number of missing persons for comparison with unknown human remains. To serve this purpose each part must: 1) derive from information garnered from the remains, and 2) provide information that missing person’s community will generally agree describes them. Observer and statistical error, sampling error, and the disconnect between biological and social categories are all associated with each step of estimation, and can render aspects of the biological profile useless, or even harmful, to the ultimate goal of identification. Here, we examine patterns of phenetic variation within a group usually treated as homogeneous, European-descended populations in the United States and Australia, usually described as “White” (n = 365). We analyzed dental morphological data for biological distances and using several classification techniques. We find that structure exists among these groups. Accuracy of classification of individuals from contemporary Tennessee approaches forensic significance, compared to samples from New York, Ohio, and Australia. These results point out that researchers commonly seek structured variation among some populations but not others. We examine this point, and make recommendations to the field to change our approach to the estimation of population affinity so that it reflects the biological variation associated with social meaning relevant in local communities. Additionally, our approach to population affinity should be iterative, reflecting cultural changes over time, and reflexive, aware and responding to our own biases.

Craniometric Variation in a Regional Sample from Antioquia, Medellín, Colombia

Population affinity estimation is frequently assessed from measurements of the cranium. Traditional models place individuals into discrete groups―such as Hispanic―that often encompass very diverse populations. Current research, including this study, challenges these assumptions using more refined population affinity estimation analyses. We examine craniometric data for a sample of individuals from different regions in Antioquia, Colombia. We first assessed the sample to understand intraregional variation in cranial shape as a function of birthplace or a culturally constructed social group label. Then, pooling the Colombian data, we compare cranial variation with global contemporary and prehistoric groups. Results did not indicate significant intraregional variation in Antioquia; classification models performed poorly (28.6% for birthplace and 36.6% for social group). When compared to other groups (American Black, American White, Asian, modern Hispanic, and prehistoric Native American), our model correctly classified 75.5% of the samples. We further refined the model by separating the pooled Hispanic sample into Mexican and Guatemalan samples, which produced a correct classification rate of 74.4%. These results indicate significant differences in cranial form among groups commonly united under the classification “Hispanic” and bolster the addition of a refined approach to population affinity estimation using craniometric data.

Fine tuning for success in structure-based virtual screening

Structure-based virtual screening plays a significant role in drug-discovery. The method virtually docks millions of compounds from corporate or public libraries into a binding site of a disease-related protein structure, allowing for the selection of a small list of potential ligands for experimental testing. Many algorithms are available for docking and assessing the affinity of compounds for a targeted protein site. The performance of affinity estimation calculations is highly dependent on the size and nature of the site, therefore a rationale for selecting the best protocol is required. To address this issue, we have developed an automated calibration process, implemented in a Knime workflow. It consists of four steps: preparation of a protein test set with structures and models of the target, preparation of a compound test set with target-related ligands and decoys, automatic test of 24 scoring/rescoring protocols for each target structure and model, and graphical display of results. The automation of the process combined with execution on high performance computing resources greatly reduces the duration of the calibration phase, and the test of many combinations of algorithms on various target conformations results in a rational and optimal choice of the best protocol. Here, we present this tool and exemplify its application in setting-up an optimal protocol for SBVS against Retinoid X Receptor alpha.

Equivariant estimation following selection from two normal populations having common unknown variance

For estimating the mean of the selected normal population (the one corresponding to the larger sample mean), we prove some admissibility and minimaxity results for the estimators belonging to the class of linear, affine and permutation equivariant estimators under the criteria of scaled mean squared error and scaled absolute bias. We derive a sufficient condition for inadmissibility of any affine and permutation equivariant estimator under the scaled mean squared error criterion. Consequently, all linear, affine and permutation equivariant estimators (except the naive estimator, larger of the two sample means) and some estimators proposed in Hsieh [On estimating the mean of the selected population with unknown variance. Commun Stat-Theor Methods. 1981;10(18):1869–1878] become inadmissible under scaled mean squared error criterion. We further show that under the scaled mean squared error criterion, the naive estimator is minimax, admissible and generalized Bayes with respect to the Jeffreys' prior. We analyse a real data set for illustration purpose and provide a simulation study to compare the performances of various competing estimators.

The linear conditional expectation in Hilbert space

The linear conditional expectation (LCE) provides a best linear (or rather, affine) estimate of the conditional expectation and hence plays an important rôle in approximate Bayesian inference, especially the Bayes linear approach. This article establishes the analytical properties of the LCE in an infinite-dimensional Hilbert space context. In addition, working in the space of affine Hilbert–Schmidt operators, we establish a regularisation procedure for this LCE. As an important application, we obtain a simple alternative derivation and intuitive justification of the conditional mean embedding formula, a concept widely used in machine learning to perform the conditioning of random variables by embedding them into reproducing kernel Hilbert spaces.

Receptor Dynamics in Molecular Recognition by Cryo-EM and Molecular Simulation.

The appropriate selection of initial receptor structure has been the "cornerstone" or foundation of successful structure-based virtual screening (SBVS), and plagued the structure-based design with a significant practical problem to determine the major physiological states or important transition states of receptors (e.g. proteins with multiple low-energy conformations and liganddependent conformational dynamics). It is well known that current SBVS methods lack the capacity to capture and characterize the intrinsic receptor flexibility with ideal cost-effectiveness. In recent years, cryoelectron microscopy (cryo-EM) has been routinely applied in the determination of biomolecular assemblies within the physiological state. In this work, we review the roles of cryo-EM and ensemble docking methods to present the intrinsically dynamic behavior of biomacromolecules, as well as the ever-improving estimation of ligand binding affinities and receptor-ligand thermodynamics. Finally, we also provide a viewpoint for further research works on modeling receptor dynamics.

Expanded Ensemble Methods Can be Used to Accurately Predict Protein-Ligand Relative Binding Free Energies.

Alchemical free energy methods have become indispensable in computational drug discovery for their ability to calculate highly accurate estimates of protein-ligand affinities. Expanded ensemble (EE) methods, which involve single simulations visiting all of the alchemical intermediates, have some key advantages for alchemical free energy calculation. However, there have been relatively few examples published in the literature of using expanded ensemble simulations for free energies of protein-ligand binding. In this paper, as a test of expanded ensemble methods, we compute relative binding free energies using the Open Force Field Initiative force field (codename "Parsley") for 24 pairs of Tyk2 inhibitors derived from a congeneric series of 16 compounds. The EE predictions agree well with the experimental values (root-mean-square error (RMSE) of 0.94 ± 0.13 kcal mol-1 and mean unsigned error (MUE) of 0.75 ± 0.12 kcal mol-1). We find that while increasing the number of alchemical intermediates can improve the phase space overlap, faster convergence can be obtained with fewer intermediates, as long as acceptance rates are sufficient. We also find that convergence can be improved using more aggressive updating of biases, and that estimates can be improved by performing multiple independent EE calculations. This work demonstrates that EE is a viable option for alchemical free energy calculation. We discuss the implications of these findings for rational drug design, as well as future directions for improvement.

IgM-bearing B cell affinity subpopulations possess differential antigen sensitivity in rainbow trout

The need for accurate assessments of in vitro generated antibody prompted examination of the effect of antigen on secreted antibody concentrations and affinities. It was found that the antigen concentrations commonly employed for in vitro stimulation were able to significantly compromise IgM titer and affinity estimates in rainbow trout. Specifically, IgM titers were underestimated with the high affinity antibodies being specifically blocked. To remedy this, pulsed antigen cultures were employed, and it was found to reveal more accurate IgM antibody titers and affinity estimates. Additionally, pulsed dose responses provided evidence that high antigen concentrations specifically suppressed high affinity B cell induction. Optimal expression of high affinity antibodies required exposure to lower concentrations of antigen. Each affinity subpopulation appeared to possess a graded sensitivity to each dose of antigen, revealing the complex dynamic for differential IgM-bearing B cell induction that is possible during a response. These results reveal not only the need for antigen removal prior to in vitro antibody secretion, but also the possible role of high zone immunological tolerance on IgM affinity maturation in rainbow trout.

Recent Developments in Free Energy Calculations for Drug Discovery.

The grand challenge in structure-based drug design is achieving accurate prediction of binding free energies. Molecular dynamics (MD) simulations enable modeling of conformational changes critical to the binding process, leading to calculation of thermodynamic quantities involved in estimation of binding affinities. With recent advancements in computing capability and predictive accuracy, MD based virtual screening has progressed from the domain of theoretical attempts to real application in drug development. Approaches including the Molecular Mechanics Poisson Boltzmann Surface Area (MM-PBSA), Linear Interaction Energy (LIE), and alchemical methods have been broadly applied to model molecular recognition for drug discovery and lead optimization. Here we review the varied methodology of these approaches, developments enhancing simulation efficiency and reliability, remaining challenges hindering predictive performance, and applications to problems in the fields of medicine and biochemistry.

Cardiolipin in the spotlight: Quantitative analysis and fluorescence-based competitive binding assay

Cardiolipin (CL) is a key phospholipid responsible for mitochondrial function and cristae integrity. The CL level is associated with various diseases characterized by mitochondrial dysfunction, including ischemic heart diseases and cancer. CL is an attractive target for mitochondria-specific drugs, but unnecessary interaction with CL might lead to detrimental side effects such as heart failure and kidney dysfunction. Thus, a simple and robust method for CL quantification and a reliable assay for the determination of drug affinity for CL are desired. We report a new fluorescent CL-specific probe with impressive photophysical properties that allows CL quantification in mitochondrial fractions isolated from cell and tissue homogenates and enables estimates of drug affinity for CL in the first fluorescence-based competitive binding assay. It was found that CL concentration is elevated in mitochondrial fractions isolated from cancer cells and cells with high proliferation rate (up to 108.5 ± 16.0 nmol/mg prot in mouse colon carcinoma cells, CT-26). CL concentration in mitochondria from brain tissue (66.11 ± 5.78 nmol/mg prot) is circa twice higher than in heart and kidney mitochondria (37.49 ± 8.69 and 33.95 ± 5.32 nmol/mg prot, respectively.) Generally, positively charged substances bind with CL, but their affinity is highly variable with EC50 values ranging from sub-micromolar to millimolar concentration.

Featurization strategies for protein–ligand interactions and their applications in scoring function development

AbstractThe predictive performance of classical scoring functions (SFs) seems to have reached a plateau. Currently, SFs relying on sophisticated machine learning techniques have shown great potential in binding affinity prediction and virtual screening. As one of the most indispensable components in the workflow of training a machine learning scoring function (MLSF), the featurization or representation process enables us to catch certain physical processes that are important for protein–ligand interactions and to obtain machine‐readable descriptors. Currently, according to how they are derived, the descriptors used in MLSFs for both continuous and binary binding affinity estimates can be grouped into two broad categories: handcrafted features and automated‐extraction features. Moreover, the automated‐extraction features emerge as a new featurization trend along with the application of deep learning algorithms. Here, we make a thorough summary of the advances in the featurization strategies for protein–ligand interactions in the context of MLSFs, with emphasis on the recently rising automated‐extraction features. We also discuss the similarity between protein–ligand interaction representations and small‐molecule representations, and the challenges confronted by the scientific community in characterizing protein–ligand interactions. We expect that this review could inspire the development of novel featurization approaches and boosted MLSFs.This article is categorized under: Data Science > Artificial Intelligence/Machine Learning Software > Molecular Modeling Molecular and Statistical Mechanics > Molecular Interactions

Search for Novel Lead Inhibitors of Yeast Cytochrome bc1, from Drugbank and COCONUT.

In this work we introduce a novel filtering and molecular modeling pipeline based on a fingerprint and descriptor similarity procedure, coupled with molecular docking and molecular dynamics (MD), to select potential novel quoinone outside inhibitors (QoI) of cytochrome bc1 with the aim of determining the same or different chromophores to usual. The study was carried out using the yeast cytochrome bc1 complex with its docked ligand (stigmatellin), using all the fungicides from FRAC code C3 mode of action, 8617 Drugbank compounds and 401,624 COCONUT compounds. The introduced drug repurposing pipeline consists of compound similarity with C3 fungicides and molecular docking (MD) simulations with final QM/MM binding energy determination, while aiming for potential novel chromophores and perserving at least an amide (R1HN(C=O)R2) or ester functional group of almost all up to date C3 fungicides. 3D descriptors used for a similarity test were based on the 280 most stable Padel descriptors. Hit compounds that passed fingerprint and 3D descriptor similarity condition and had either an amide or an ester group were submitted to docking where they further had to satisfy both Chemscore fitness and specific conformation constraints. This rigorous selection resulted in a very limited number of candidates that were forwarded to MD simulations and QM/MM binding affinity estimations by the ORCA DFT program. In this final step, stringent criteria based on (a) sufficiently high frequency of H-bonds; (b) high interaction energy between protein and ligand through the whole MD trajectory; and (c) high enough QM/MM binding energy scores were applied to further filter candidate inhibitors. This elaborate search pipeline led finaly to four Drugbank synthetic lead compounds (DrugBank) and seven natural (COCONUT database) lead compounds—tentative new inhibitors of cytochrome bc1. These eleven lead compounds were additionally validated through a comparison of MM/PBSA free binding energy for new leads against those obtatined for 19 QoIs.

Abstract 21: LiP-MS, a machine learning-based chemoproteomic approach to identify drug targets in complex proteomes

Abstract Background Target identification is a critical step in elucidating the mechanism of action (MoA) for bioactive compounds. For target-based and phenotypic drug discovery pipelines, extensive potential target knowledge for a lead compound provides essential insights that enable potency and selectivity optimization. The tedious process of target deconvolution for a compound often necessitates a plethora of biochemical and biophysical techniques. To expand the toolbox of unbiased target ID approaches, we developed a novel workflow combining limited proteolysis with quantitative mass spectrometry (LiP-MS) that exploits the drug binding phenomena of protein structural alterations and steric hindrance. Advantageously, LiP-MS's unique peptide-centric focus exploits signature peptide detection to discern ligand binding. Additionally, the LiP-MS workflow enables binding affinity estimation (EC50) and binding site prediction. Here we demonstrate the performance of LiP-MS using two well-characterized kinase inhibitors (KIs), an AstraZeneca CDK9 inhibitor (AZ) and Selumetinib (SE). Methods Mechanically sheared HeLa or U2OS cell lysate was incubated with compound at multiple concentrations. Next, a limited proteolytic digest was performed using proteinase K. After quenching this digestion, lysate was trypsin digested to peptides for mass spectrometry analysis. The resulting peptides were analyzed quantitatively using data-independent acquisition (DIA)-MS. Results Herein, we use LiP-MS to unbiasedly identify unique peptides generated by the binding of two distinct classes of kinase inhibitors in human cell lysate. For the ATP-competitive inhibitor AZ, LiP-Quant shows a strong enrichment for CDKs in the target space defined by LiP score, including CDK1, 2, 4, 6, 9 and 11A. In addition, our data indicates that this KI targets members of the CDKs with different selectivity, with CDK9 displaying the highest compound affinity (nM range). For Selumetinib, a non-competitive allosteric inhibitor, LiP-MS clearly identified the direct targets MEK1/2 as the main hits by LiP score. Both cases represent a highly specific enrichment given that we quantified > 120,000 peptides in each of the experiments. These findings confirm our approach's ability to identify genuine drug targets regardless of drug MoA in a complex biological matrix. Importantly, in both KI target ID studies, identified LiP peptides could be successfully deployed to map compound binding site, demonstrating the potential of LiP-MS to pinpoint regions involved in drug-protein interactions. Conclusions Collectively, this data demonstrates that LiP-MS can be used to effectively identify protein drug targets and characterize the binding properties in complex proteomes independent of the compound's MoA and without compound modification or labeling. These capabilities make LiP-MS a powerful addition to the target ID toolbox. Citation Format: Nigel Beaton, Yuehan Feng, Roland Bruderer, Adam Hendricks, Ghaith Hamza, Eric Miele, Rick Davies, Kristina Beeler, Ilaria Piazza, Paola Picotti, Paola Castaldi, Lukas Reiter. LiP-MS, a machine learning-based chemoproteomic approach to identify drug targets in complex proteomes [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 21.

Context-Based Inter Mode Decision Method for Fast Affine Prediction in Versatile Video Coding

Versatile Video Coding (VVC) is the most recent video coding standard developed by Joint Video Experts Team (JVET) that can achieve a bit-rate reduction of 50% with perceptually similar quality compared to the previous method, namely High Efficiency Video Coding (HEVC). Although VVC can support the significant coding performance, it leads to the tremendous computational complexity of VVC encoder. In particular, VVC has newly adopted an affine motion estimation (AME) method to overcome the limitations of the translational motion model at the expense of higher encoding complexity. In this paper, we proposed a context-based inter mode decision method for fast affine prediction that determines whether the AME is performed or not in the process of rate-distortion (RD) optimization for optimal CU-mode decision. Experimental results showed that the proposed method significantly reduced the encoding complexity of AME up to 33% with unnoticeable coding loss compared to the VVC Test Model (VTM).

Evaluating population affinity estimates in forensic anthropology: Insights from the forensic anthropology database for assessing methods accuracy (FADAMA).

Using a sample of anonymized U.S. forensic anthropology cases (n=251) from the FADAMA database, we assess the degree of concordance between decedents' social identifiers and anthropologists' continental-based classifications. We report high success rates (>90%) that generally support previous findings, yet we acknowledge the limitations of assessing "ancestry" accuracy based on resolved cases and draw attention to situations in which our methods fail. For example, forensic anthropologists achieve just 20% accuracy when classifying individuals as "other" or "mixed"-problematic categories that we argue should be rejected. Leveraging our findings, we ask: what are we really estimating when we perform a skeletal assessment of "ancestry" in the US context? We argue that the "ancestry estimates" historically and routinely produced in forensic anthropology instead give information on population affinity: a measure of how similar a given case is to one among several socially relevant groups of interest. Distancing forensic anthropology from genetics and other disciplines that estimate ancestry, the approach of population affinity assesses similarities to both social and biological groupings, potentially at a fine-grained level, attempting to account for the complex histories, shared biologies, and wide ranges of diversity that characterize our communities and our casework. Population affinity is a flexible and inclusive approach that more accurately describes current forensic anthropological analyses of human variation. Going forward, we must acknowledge and build on the contributions of previous scholars as we work together toward our shared goal of theoretically grounded analyses of human variation that accurately and equitably serve all casework decedents.

Map3D: Registration-Based Multi-Object Tracking on 3D Serial Whole Slide Images.

There has been a long pursuit for precise and reproducible glomerular quantification on renal pathology to leverage both research and practice. When digitizing the biopsy tissue samples using whole slide imaging (WSI), a set of serial sections from the same tissue can be acquired as a stack of images, similar to frames in a video. In radiology, the stack of images (e.g., computed tomography) are naturally used to provide 3D context for organs, tissues, and tumors. In pathology, it is appealing to do a similar 3D assessment. However, the 3D identification and association of large-scale glomeruli on renal pathology is challenging due to large tissue deformation, missing tissues, and artifacts from WSI. In this paper, we propose a novel Multi-object Association for Pathology in 3D (Map3D) method for automatically identifying and associating large-scale cross-sections of 3D objects from routine serial sectioning and WSI. The innovations of the Multi-Object Association for Pathology in 3D (Map3D) method are three-fold: (1) the large-scale glomerular association is formed as a new multi-object tracking (MOT) perspective; (2) the quality-aware whole series registration is proposed to not only provide affinity estimation but also offer automatic kidney-wise quality assurance (QA) for registration; (3) a dual-path association method is proposed to tackle the large deformation, missing tissues, and artifacts during tracking. To the best of our knowledge, the Map3D method is the first approach that enables automatic and large-scale glomerular association across 3D serial sectioning using WSI. Our proposed method Map3D achieved MOTA = 44.6, which is 12.1% higher than the non-deep learning benchmarks.