Estimation Of Affinity Research Articles

Estimation of population affinity using cranial measurements acquired in multidetector computed tomography images of Japanese and Malay individuals.

It is imperative in a forensic investigation to determine the identity of an unidentified corpse, for which a crucial starting point is to establish population affinity as part of the biological profile supplied by the forensic anthropologist. The present study investigates the feasibility of using multidetector computed tomography (MDCT) images to quantify craniometric variation between Japanese and Malay populations relative to the estimation of population affinity in a forensic context. The Japanese and Malay samples comprise MDCT scans of 252 (122 female; 130 male) and 182 (84 female; 98 male) adult individuals, respectively. A total of 18 measurements were acquired, and two machine learning methods (random forest modeling, RFM; support vector machine, SVM) were applied to classify population affinity. The accuracy of the two-way pooled-sex model was 88.0% for RFM and 94.5% for SVM, respectively. The four-way population and sex model produced an overall classification accuracy of 81.3% for RFM and 91.7% for SVM. The sex-specific models of population affinity showed correct rates of classification of more than 90% in both females (90.8% for RFM and 97.6% for SVM) and males (91.2% for RFM and 97.4% for SVM). Our findings clearly indicate that the cranial measurements acquired in MDCT images can be used for the forensic classification of Japanese and Malay individuals and thus serve as a reference for forensic anthropologists attempting to identify unidentified remains.

Molecular Docking of Endolysins for Studying Peptidoglycan Binding Mechanism.

Endolysins of bacteriophages, which degrade the bacterial cell wall peptidoglycan, are applicable in many industries to deal with biofilms and bacterial infections. While multi-domain endolysins have both enzymatically active and cell wall-binding domains, single-domain endolysins consist only of an enzymatically active domain, and their mechanism of peptidoglycan binding remains unexplored, for this is a challenging task experimentally. This research aimed to explore the binding mechanism of endolysins using computational approaches, namely molecular docking and bioinformatical tools, and analyze the performance of these approaches. The docking engine Autodock Vina 1.1.2 and the 3D-RISM module of AmberTools 24 were studied in the current work and used for receptor-ligand affinity and binding energy calculations, respectively. Two possible mechanisms of single-domain endolysin-ligand binding were predicted by Autodock Vina and verified by the 3D-RISM. As a result, the previously obtained experimental results on peptidoglycan binding of the isolated gamma phage endolysin PlyG enzymatically active domain were supported by molecular docking. Both methods predicted that single-domain endolysins are able to bind peptidoglycan, with Autodock Vina being able to give accurate numerical estimates of protein-ligand affinities and 3D-RISM providing comparative values.

Probing marine macroalgal phlorotannins as an antibacterial candidate against Salmonella typhi: Molecular docking and dynamics simulation approach

The increasing prevalence of drug-resistant bacterial strains, including multidrug-resistant Salmonella typhi, has raised significant concerns about the effectiveness of traditional antimicrobial treatments. To address this issue, the study employed computational techniques to evaluate the potential of natural phlorotannins derived from marine algae as alternative antibacterial agents against S. typhi. A total of 104 phlorotannins were retrieved from PubChem and subjected to molecular docking with three specific target proteins of S. typhi. Among the compounds tested, Compound-10 a derivative of dieckol exhibited the highest affinity for the Omptin family outer membrane protease [pgtE] protein and for the cell invasion protein sipB protein. The docking results revealed strong interactions between the phlorotannins and the target proteins, indicating their potential as antimicrobial agents. Additionally, pharmacokinetic studies using the QikProp module demonstrated that the top-ranked compounds showed favourable drug-like properties with good druggable efficiency, moderate gut-blood barrier transport, and high human oral absorption. Most compounds passed the rule of three and five drug-likeness criteria, indicating their potential as drug candidates. Furthermore, MM-GBSA analysis provided relative binding affinity estimations, ranking the compounds based on their calculated binding energies. These results align reasonably well with experimental binding affinities, reinforcing the potential of the identified compounds as potent binders to their target proteins. This study highlights the promising antibacterial potential of marine phlorotannins against S. typhi. Further in vitro studies are warranted to validate these compounds' efficacy and anti-microbial activity, ultimately paving the way for developing new therapeutic strategies to combat drug-resistant Salmonella infections.

Exploring the anti-inflammatory potential of phytochemicals against STING::TBK1 pathway

ABSTRACT Viral illnesses frequently cause excessive inflammation in the body, which adds to the severity of symptoms and problems. Understanding and regulating this inflammatory response is critical for successful viral infection management and therapy. It is traditional Ayurvedic understanding to reduce inflammation with formulations of Tulsi, Dalchini, Sunthi, and Krishan mirch. Using computer-aided drug discovery methodologies, this study aims to find phytochemicals that interact with TBK1 and are likely to have this anti-inflammatory effect. Molecular docking results revealed that Oleanolic acid, Astiaticoside A, Cepharadione A, Proanthrocyanidin, and Alpha carotene had binding scores of −8.1 kcal/mol, −7.8 kcal/mol, −6.8 kcal/mol, −6.75 kcal/mol, and −6.8 kcal/mol against TBK1, respectively. 20 ns MD simulations of TBK1 in complex with phytochemicals were performed to evaluate complex stability and protein conformational changes. The simulations showed stable complexes with favourable energy profiles, as evidenced by characteristics such as RMSD, RMSF, Rg, and hydrogen bonding. The MM-PBSA analysis produced precise binding affinity estimates. This work emphasises the potential of these phytochemicals and the need for additional in vitro and in vivo validation.

Differences in the Shape of the Frontal Bone between 20th-Century Euro-Americans and Germans

Geometric morphometrics is a very useful but rarely applied concept in forensic anthropology. It uses information on the shape of an object for data analysis. For this study, geometric morphometrics has been used to compare the shape of the frontal bone between a German and a Euro-American sample (both early 20th century). Results were compared using size-only, shape-only and size-and-shape combined. Results show that the frontal shapes of the two study populations can clearly be distinguished from one another. The best classification results could be achieved when combining size and shape data for analysis. Centroid size showed significant variation by group and sex, and that Euro-Americans are more sex dimorphic than Germans. We conclude that shape data provides a considerable amount of extra information for population affinity estimation, even on a single cranial bone like the frontal. A broader application of geometric morphometrics in forensic anthropology could thus help generating a more reliable biological profile, as well as providing additional insight into German-American morphometric differentiation.

Steven D. Ousley

Early in his career, Steven D. Ousley established his view on the need for biological anthropologists to provide empirical support for their findings. He, like many of his mentors and contemporaries, became acutely aware of the need for more statistical rigor in forensic anthropology. His research and the scholarly products he produced explored the relationship and linkage between method and theory, between what can be observed, what can be explained, and what can be quantified. Driven by an insatiable curiosity and a commitment to advancing knowledge, Steve’s work has left an indelible mark on the intersection between biological anthropology and statistical approaches to the study of human variation and has enriched our understanding of population affinity estimation. With a career characterized by rigor, innovation, and a passion for discovery, his pioneering insights continue to reverberate through academia and the applied world encompassing forensic anthropology. As we delve into this collection of scholarly works, we embark on a journey through the intellectual landscape Steve so profoundly shaped, celebrating his legacy and the lasting impact of his ideas. We present herewith a compilation of Steve’s works, thesis, dissertation, published papers, grants and awards, software, student mentorships, and data collection activities. These illustrate the breadth of his knowledge and contributions to our discipline.

Amalgamated Pharmacoinformatics Study to Investigate the Mechanism of Xiao Jianzhong Tang against Chronic Atrophic Gastritis.

Traditional Chinese medicine (TCM) Xiao Jianzhong Tang (XJZ) has a favorable efficacy in the treatment of chronic atrophic gastritis (CAG). However, its pharmacological mechanism has not been fully explained. The purpose of this study was to find the potential mechanism of XJZ in the treatment of CAG using pharmacocoinformatics approaches. Network pharmacology was used to screen out the key compounds and key targets, MODELLER and GNNRefine were used to repair and refine proteins, Autodock vina was employed to perform molecular docking, Δ Lin_F9XGB was used to score the docking results, and Gromacs was used to perform molecular dynamics simulations (MD). Kaempferol, licochalcone A, and naringenin, were obtained as key compounds, while AKT1, MAPK1, MAPK14, RELA, STAT1, and STAT3 were acquired as key targets. Among docking results, 12 complexes scored greater than five. They were run for 50ns MD. The free binding energy of AKT1-licochalcone A and MAPK1-licochalcone A was less than -15 kcal/mol and AKT1-naringenin and STAT3-licochalcone A was less than -9 kcal/mol. These complexes were crucial in XJZ treating CAG. Our findings suggest that licochalcone A could act on AKT1, MAPK1, and STAT3, and naringenin could act on AKT1 to play the potential therapeutic effect on CAG. The work also provides a powerful approach to interpreting the complex mechanism of TCM through the amalgamation of network pharmacology, deep learning-based protein refinement, molecular docking, machine learning-based binding affinity estimation, MD simulations, and MM-PBSA-based estimation of binding free energy.

Determination of Protein-Ligand Binding Affinities by Thermal Shift Assay.

Quantification of protein-ligand interactions is crucial for understanding the protein's biological function and for drug discovery. In this study, we employed three distinct approaches for determination of protein-ligand binding affinities by a thermal shift assay using a single ligand concentration. We present the results of the comparison of the performance of the conventional curve fitting (CF) method and two newly introduced methods - assuming zero heat capacity change across small temperature ranges (ZHC) and utilizing the unfolding equilibrium constant (UEC); the latter has the advantage of reducing calculations by obtaining the unfolding equilibrium constant directly from the experimental data. Our results highlight superior performance of the ZHC and UEC methods over the conventional CF method in estimating the binding affinity, irrespective of the ligand concentration. In addition, we evaluated how the new methods can be applied to high-throughput screening for potential binders, when the enthalpy (ΔH L) and molar heat capacity change (ΔC PL) of ligand binding are unknown. Our results suggest that, in this scenario, using the -300 cal K-1 mol-1 assumption for ΔC pL and either -5 kcal mol-1 or the average enthalpy efficiency-based estimation for ΔH L(T) can still provide reasonable estimates of the binding affinity. Incorporating the new methods into the workflow for screening of small drug-like molecules, typically conducted using single-concentration libraries, could greatly simplify and streamline the drug discovery process.

A New Real-Time Simple Method to Measure the Endogenous Nitrate Reductase Activity (Nar) in Paracoccus denitrificans and Other Denitrifying Bacteria.

The transmembrane nitrate reductase (Nar) is the first enzyme in the dissimilatory alternate anaerobic nitrate respiratory chain in denitrifying bacteria. To date, there has been no real-time method to determine its specific activity embedded in its native membrane; here, we describe such a new method, which is useful with the inside-out membranes of Paracoccus denitrificans and other denitrifying bacteria. This new method takes advantage of the native coupling of the endogenous NADH dehydrogenase or Complex I with the reduction of nitrate by Nar through the quinone pool of the inner membranes of P. denitrificans. This is achieved under previously reached anaerobic conditions. Inner controls confirming the specific Nar activity determined by this new method were made by the total inhibition of the Nar enzyme by sodium azide and cyanide, well-known Nar inhibitors. The estimation of the Michaelis-Menten affinity of Nar for NO3- using this so-called Nar-JJ assay gave a Km of 70.4 μM, similar to previously determined values. This new Nar-JJ assay is a suitable, low-cost, and reproducible method to determine in real-time the endogenous Nar activity not only in P. denitrificans, but in other denitrifying bacteria such as Brucella canis, and potentially in other entero-pathogenic bacteria.

A time‐efficient computational binding affinity estimation protocol with utilization of limited experimental data: A case study for adenosine receptor

AbstractEstimating binding affinity is a crucial step in the drug discovery process. In computer‐aided drug design, this challenge can be divided into two main tasks: finding the correct binding pose and estimating the binding free energy. In this study, we propose a new binding affinity estimation protocol that utilizes molecular docking with limited experimental data and estimates binding affinity using molecular dynamics simulation. A custom scoring function was employed during docking to identify an improved initial binding pose, and the linear interaction energy method with an optimized coefficient was used for binding affinity estimation. The protocol was validated with an external data set and applied to modafinil and its derivatives to rank their binding affinities to adenosine A2A receptors (ADORA2A) as a case study. This approach could be both time‐efficient and valuable for computational drug discovery, particularly when experimental data is limited.

UnWarpME: Unsupervised warping map estimation in real-world scenarios

This paper introduces a novel approach to warp an RGB image to a new target pose, where it learns to inpaint the invisible parts of the image by sampling pixels from the visible parts. This technique is particularly relevant in applications such as novel pose image generation, where the quality of the generated samples heavily relies on the warped images. Conventional methods typically utilize an affine warping map estimator and learn to estimate the warping map through a downstream image generation task. However, this approach results in an affine function being returned regardless of the complexity of the deformation between the source and target poses. In contrast, our proposed method involves estimating the warping map using a convolutional function, which learns through alternating between two downstream tasks. Initially, the estimation treats the warping as part of an image generation task, similar to existing methods but without the constraint of an affine transformation. This encourages the estimation of complex transformations beyond affine deformations. Subsequently, disregarding the image generation task, we supervise the convolutional warping map to learn any potential affine transformation between the guiding poses of the sample. Our experimental results demonstrate the high effectiveness of our method in preserving image textures while transforming it into highly complex poses.

Structural analysis and shape-based identification of novel inhibitors targeting the Trypanosoma cruzi proteasome

There is an urgent need to develop new, safer, and more effective drugs against Chagas disease (CD) as well as related kinetoplastid diseases. Targeting and inhibiting the Trypanosoma cruzi proteasome has emerged as a promising therapeutic approach in this context. To expand the chemical space for this class of inhibitors, we performed virtual screening campaigns with emphasis on shape-based similarity and ADMET prioritization. We describe the ideation and application of robustly validated shape queries for these campaigns, which furnished 44 compounds for biological evaluation. Five hit compounds demonstrated in vitro antitrypanosomal activity by potential inhibition of T. cruzi proteasome and notable chemical diversities, particularly, LCQFTC11. Structural insights were achieved by homology modeling, sequence/structure alignment, proteasome-species comparison, docking, molecular dynamics, and MMGBSA binding affinity estimations. These methods confirmed key interactions as well as the stability of LCQFTC11 at the β4/β5 subunits' binding site of the T. cruzi proteasome, consistent with known inhibitors. Our results warrant future assay confirmation of our hit as a T. cruzi proteasome inhibitor. Importantly, we also shed light into dynamic details for a proteasome inhibition mechanism that shall be further investigated. We expect to contribute to the development of viable CD drug candidates through such a relevant approach.

Determining the Affinity and Kinetics of Small Molecule Inhibitors of Galectin-1 Using Surface Plasmon Resonance.

The beta-galactoside-binding mammalian lectin galectin-1 can bind, via its carbohydrate recognition domain (CRD), to various cell surface glycoproteins and has been implicated in a range of cancers. As a consequence of binding to sugar residues on cell surface receptors, it has been shown to have a pleiotropic effect across many cell types and mechanisms, resulting in immune system modulation and cancer progression. As a result, it has started to become a therapeutic target for both small and large molecules. In previous studies, we used fluorescence polarization (FP) assays to determine KD values to screen and triage small molecule glycomimetics that bind to the galectin-1 CRD. In this study, surface plasmon resonance (SPR) was used to compare human and mouse galectin-1 affinity measures with FP, as SPR has not been applied for compound screening against this galectin. Binding affinities for a selection of mono- and di-saccharides covering a 1000-fold range correlated well between FP and SPR assay formats for both human and mouse galectin-1. It was shown that slower dissociation drove the increased affinity at human galectin-1, whilst faster association was responsible for the effects in mouse galectin-1. This study demonstrates that SPR is a sound alternative to FP for early drug discovery screening and determining affinity estimates. Consequently, it also allows association and dissociation constants to be measured in a high-throughput manner for small molecule galectin-1 inhibitors.

Virtual screening of cucurbituril host-guest complexes: Large-scale benchmark of end-point protocols under MM and QM Hamiltonians

The similarity of protein–ligand and host–guest complexes leads to the direct extension of end-point free energy techniques to virtual screening of the latter. Despite the massive application of end-point calculations in modelling and predictions of host–guest binding, it still lacks a large-scale benchmark test with sufficient sizes, sampling length and diverse coverages of chemical spaces to get rid of many disturbing factors and provide an unbiased statistical perspective of practical performance and value of end-point screening. Cucurbiturils as one of the main-stream macrocycles are quite popular in pharmaceutical research and industrial applications as drug reservoirs and carriers. In this work, we gather a large dataset of ∼150 host–guest complexes involving cucurbiturils with varying numbers of repeating units, extensively sample the conformational space in physical end states with both the single- and three-trajectory protocols, and extract the affinity estimates with a variety of MM and QM/GBSA Hamiltonians with implicit solvents. The diversity of chemical compositions, the quality of configurational sampling, and the comprehensiveness of parameter combinations are unprecedently high, enabling us to deduce conclusive pictures of the end-point performance in host–guest binding. Unlike previous reports on improved performance due to the incorporation of the QM treatment, shifting to QM/GBSA treatments deteriorates both the reproduction of absolute affinities and the prediction of affinity ranks in our large-scale end-point benchmark, regardless of the employed sampling protocol (i.e., single- and three-trajectory realization). Overall, in the end-point screening of cucurbituril host–guest complexes, we recommend sticking to the original MM/GBSA Hamiltonians in both sampling and free energy estimation. The best/recommended selection with high accuracy and robustness among all end-point protocols benchmarked in this work is the three-trajectory GAFF + GBOBC-I regime, which achieves top performances for both the reproduction of absolute affinities and ranking different host–guest pairs. Docking estimates with seven main-stream protocols have been observed as a promising tool for host–guest binding, but their ranking powers in the current large host–guest dataset does not surpass end-point tools, validating the value of post-docking end-point screening.

Estimation of population affinity using proximal femoral measurements based on computed tomographic images in the Japanese and western Australian populations

The present study analyzes morphological differences femora of contemporary Japanese and Western Australian individuals and investigates the feasibility of population affinity estimation based on computed tomographic (CT) data. The latter is deemed to be of practical importance because most anthropological methods rely on the assessment of aspects of skull morphology, which when damaged and/or unavailable, often hampers attempts to estimate population affinity. The study sample comprised CT scans of 297 (146 females; 151 males) Japanese and 330 (145 females; 185 males) Western Australian adult individuals. A total of 10 measurements were acquired in two-dimensional CT images of the left and right femora; two machine learning methods (random forest modeling [RFM]) and support vector machine [SVM]) were then applied for population affinity classification. The accuracy of the two-way (sex-specific and sex-mixed) model was between 71.38 and 82.07% and 76.09–86.09% for RFM and SVM, respectively. Sex-specific (female and male) models were slightly more accurate compared to the sex-mixed models; there were no considerable differences in the correct classification rates between the female- and male-specific models. All the classification accuracies were higher in the Western Australian population, except for the male model using SVM. The four-way sex and population affinity model had an overall classification accuracy of 74.96% and 79.11% for RFM and SVM, respectively. The Western Australian females had the lowest correct classification rate followed by the Japanese males. Our data indicate that femoral measurements may be particularly useful for classification of Japanese and Western Australian individuals.

Modern machine‐learning for binding affinity estimation of protein–ligand complexes: Progress, opportunities, and challenges

AbstractStructure‐based drug design is a widely applied approach in the discovery of new lead compounds for known therapeutic targets. In most structure‐based drug design applications, the docking procedure is considered the crucial step. Here, a potential ligand is fitted into the binding site, and a scoring function assesses its binding capability. With the rise of modern machine‐learning in drug discovery, novel scoring functions using machine‐learning techniques achieved significant performance gains in virtual screening and ligand optimization tasks on retrospective data. However, real‐world applications of these methods are still limited. Missing success stories in prospective applications are one reason for this. Additionally, the fast‐evolving nature of the field makes it challenging to assess the advantages of each individual method. This review will highlight recent strides toward improved real world applicability of machine‐learning based scoring, enabling a better understanding of the potential benefits and pitfalls of these functions on a project. Furthermore, a systematic way of classifying machine‐learning based scoring that facilitates comparisons will be presented.This article is categorized under: Data Science > Chemoinformatics Data Science > Artificial Intelligence/Machine Learning Software > Molecular Modeling

Exploring cranial macromorphoscopic variation and classification accuracy in a South African sample

To date South African forensic anthropologists are only able to successfully apply a metric approach to estimate population affinity when constructing a biological profile from skeletal remains. While a non-metric, or macromorphoscopic approach exists, limited research has been conducted to explore its use in a South African population. This study aimed to explore 17 cranial macromorphoscopic traits to develop improved methodology for the estimation of population affinity among black, white and coloured South Africans and for the method to be compliant with standards of best practice. The trait frequency distributions revealed substantial group variation and overlap, and not a single trait can be considered characteristic of any one population group. Kruskal-Wallis and Dunn’s tests demonstrated significant population differences for 13 of the 17 traits. Random forest modelling was used to develop classification models to assess the reliability and accuracy of the traits in identifying population affinity. Overall, the model including all traits obtained a classification accuracy of 79% when assessing population affinity, which is comparable to current craniometric methods. The variable importance indicates that all the traits contributed some information to the model, with the inferior nasal margin, nasal bone contour, and nasal aperture shape ranked the most useful for classification. Thus, this study validates the use of macromorphoscopic traits in a South African sample, and the population-specific data from this study can potentially be incorporated into forensic casework and skeletal analyses in South Africa to improve population affinity estimates.

MaMD Analytical 1.0

We outline the functionalities and application of MaMD Analytical—a new, freely available software package for the estimation of population affinity using human cranial macromorphoscopic (MMS) traits. MaMD Analytical captures MMS scores using line drawings following the procedures outlined by Hefner and Linde (2018). MaMD Analytical generates classifications (with estimated likelihoods) into forensically significant populations using an artificial neural network and reference samples drawn from the Macromorphoscopic Databank (MaMD). Summary data (sensitivity, specificity, x-validated classification accuracies) are provided. In this article, we apply MaMD Analytical to a large sample of identified individuals not used in the original model building to assess utility and demonstrate the typical outputs for MaMD Analytical. MaMD Analytical facilitates construction of the biological profile and provides a number of safeguards in summary statistics as a valuable addition to the forensic anthropological analysis toolkit. MaMD Analytical is written in the open-source R environment integrating a previously developed artificial neural network model to estimate population affinity using well-documented and validated approaches.

On extreme quantile region estimation under heavy-tailed elliptical distributions

Consider the estimation of an extreme quantile region corresponding to a very small probability. Estimation of extreme quantile regions is important but difficult since extreme regions contain only a few or no observations. In this article, we propose an affine equivariant extreme quantile region estimator for heavy-tailed elliptical distributions. The estimator is constructed by extending a well-known univariate extreme quantile estimator. Consistency of the estimator is proved under estimated location and scatter. The practicality of the developed estimator is illustrated with simulations and a real data example.

Computational Mutagenesis of Antibody Fragments: Disentangling Side Chains from ΔΔG Predictions.

The development of highly potent antibodies and antibody fragments as binding agents holds significant implications in fields such as biosensing and biotherapeutics. Their binding strength is intricately linked to the arrangement and composition of residues at the binding interface. Computational techniques offer a robust means to predict the three-dimensional structure of these complexes and to assess the affinity changes resulting from mutations. Given the interdependence of structure and affinity prediction, our objective here is to disentangle their roles. We aim to evaluate independently six side-chain reconstruction methods and ten binding affinity estimation techniques. This evaluation was pivotal in predicting affinity alterations due to single mutations, a key step in computational affinity maturation protocols. Our analysis focuses on a data set comprising 27 distinct antibody/hen egg white lysozyme complexes, each with crystal structures and experimentally determined binding affinities. Using six different side-chain reconstruction methods, we transformed each structure into its corresponding mutant via in silico single-point mutations. Subsequently, these structures undergo minimization and molecular dynamics simulation. We therefore estimate ΔΔG values based on the original crystal structure, its energy-minimized form, and the ensuing molecular dynamics trajectories. Our research underscores the critical importance of selecting reliable side-chain reconstruction methods and conducting thorough molecular dynamics simulations to accurately predict the impact of mutations. In summary, our study demonstrates that the integration of conformational sampling and scoring is a potent approach to precisely characterizing mutation processes in single-point mutagenesis protocols and crucial for computational antibody design.