Advanced Computational Methods Research Articles

Removing geographic boundaries from liver allocation: A method for designing continuous distribution scores.

The Organ Procurement and Transplantation Network (OPTN) is eliminating geographic boundaries in liver allocation, in favor of continuous distribution. Continuous distribution allocates organs via a composite allocation score (CAS): a weighted sum of attributes like medical urgency, candidate biology, and placement efficiency. The opportunity this change represents, to include new variables and features for prioritizing candidates, will require lengthy and contentious discussions to establish community consensus. Continuous distribution could instead be implemented rapidly by computationally translating the allocation priorities for pediatric, status 1, and O/B blood type liver candidates that are presently implemented via geographic boundaries into points and weights in a CAS. Using simulation with optimization, we designed a CAS that is minimally disruptive to existing prioritizations, and that eliminates geographic boundaries and minimizes waitlist deaths without harming vulnerable populations. Compared with Acuity Circles (AC) in a 3-year simulation, our optimized CAS decreased deaths from 7771.2 to 7678.8 while decreasing average (272.66 NM vs. 264.30 NM) and median (201.14 NM vs. 186.49 NM) travel distances. Our CAS increased travel only for high MELD and status 1 candidates (423.24 NM vs. 298.74 NM), and reduced travel for other candidates (198.98 NM vs. 250.09 NM); overall travel burden decreased. Our CAS reduced waitlist deaths by sending livers for high-MELD and status 1 candidates farther, while keeping livers for lower MELD candidates nearby. This advanced computational method can be applied again after wider discussions of adding new priorities conclude; our method designs score weightings to achieve any specified feasible allocation outcomes.

Computational Approaches for Organic Semiconductors: From Chemical and Physical Understanding to Predicting New Materials.

While a complete understanding of organic semiconductor (OSC) design principles remains elusive, computational methods─ranging from techniques based in classical and quantum mechanics to more recent data-enabled models─can complement experimental observations and provide deep physicochemical insights into OSC structure-processing-property relationships, offering new capabilities for in silico OSC discovery and design. In this Review, we trace the evolution of these computational methods and their application to OSCs, beginning with early quantum-chemical methods to investigate resonance in benzene and building to recent machine-learning (ML) techniques and their application to ever more sophisticated OSC scientific and engineering challenges. Along the way, we highlight the limitations of the methods and how sophisticated physical and mathematical frameworks have been created to overcome those limitations. We illustrate applications of these methods to a range of specific challenges in OSCs derived from π-conjugated polymers and molecules, including predicting charge-carrier transport, modeling chain conformations and bulk morphology, estimating thermomechanical properties, and describing phonons and thermal transport, to name a few. Through these examples, we demonstrate how advances in computational methods accelerate the deployment of OSCsin wide-ranging technologies, such as organic photovoltaics (OPVs), organic light-emitting diodes (OLEDs), organic thermoelectrics, organic batteries, and organic (bio)sensors. We conclude by providing an outlook for the future development of computational techniques to discover and assess the properties of high-performing OSCs with greater accuracy.

Syntactic Pattern Recognition for the Prediction of L-Type Pseudoknots in RNA

The observation and analysis of RNA molecules have proved crucial for the understanding of various processes in nature. Scientists have mined knowledge and drawn conclusions using experimental methods for decades. Leveraging advanced computational methods in recent years has led to fast and more accurate results in all areas of interest. One highly challenging task, in terms of RNA analysis, is the prediction of its structure, which provides valuable information about how it transforms and operates numerous significant tasks in organisms. In this paper, we focus on the prediction of the 2-D or secondary structure of RNA, specifically, on a rare but yet complex type of pseudoknot, the L-type pseudoknot, extending our previous framework specialized for H-type pseudoknots. We propose a grammar-based framework that predicts all possible L-type pseudoknots of a sequence in a reasonable response time, leveraging also the advantages of core biological principles, such as maximum base pairs and minimum free energy. In order to evaluate the effectiveness of our methodology, we assessed four performance metrics: precision; recall; Matthews correlation coefficient (MCC); and F1-score, which is the harmonic mean of precision and recall. Our methodology outperformed the other three well known methods in terms of Precision, with a score of 0.844, while other methodologies scored 0.500, 0.333, and 0.308. Regarding the F1-score, our platform scored 0.671, while other methodologies scored 0.661, 0.449, and 0.449. The proposed methodology surpassed all methods in terms of the MCC metric, achieving a score of 0.521. The proposed method was added to our RNA toolset, which aims to enhance the capabilities of biologists in the prediction of RNA motifs, including pseudoknots, and holds the potential to be applied in a multitude of biological domains, including gene therapy, drug design, and comprehending RNA functionality. Furthermore, the suggested approach can be employed in conjunction with other methodologies to enhance the precision of RNA structure prediction.

Big Data in Oncology Nursing Research: State of the Science

ObjectiveTo review the state of oncology nursing science as it pertains to big data. The authors aim to define and characterize big data, describe key considerations for accessing and analyzing big data, provide examples of analyses of big data in oncology nursing science, and highlight ethical considerations related to the collection and analysis of big data. Data SourcesPeer-reviewed articles published by investigators specializing in oncology, nursing, and related disciplines. ConclusionBig data is defined as data that are high in volume, velocity, and variety. To date, oncology nurse scientists have used big data to predict patient outcomes from clinician notes, identify distinct symptom phenotypes, and identify predictors of chemotherapy toxicity, among other applications. Although the emergence of big data and advances in computational methods provide new and exciting opportunities to advance oncology nursing science, several challenges are associated with accessing and using big data. Data security, research participant privacy, and the underrepresentation of minoritized individuals in big data are important concerns. Implications for Nursing PracticeWith their unique focus on the interplay between the whole person, the environment, and health, nurses bring an indispensable perspective to the interpretation and application of big data research findings. Given the increasing ubiquity of passive data collection, all nurses should be taught the definition, characteristics, applications, and limitations of big data. Nurses who are trained in big data and advanced computational methods will be poised to contribute to guidelines and policies that preserve the rights of human research participants.

An advanced computational method for studying drug nanonization using green supercritical-based processing for improvement of pharmaceutical bioavailability in aqueous media

In this study, we implemented and compared various non-mechanistic based models for prediction of drug solubility in supercritical solvent. The data were collected from references and the models were built considering various operational circumstances. Small data sets, like the solubility data used in this study, have always been one of the challenges for modeling in machine learning method. In this study, in order to solve the regression problem related to the solubility of drugs, which includes 32 laboratory data, we implemented and studied models that are naturally compatible with very small data like solubility data of drugs in solvents. These models included Random Forest (RF), KNN and Extra Tree (ET). After obtaining the best settings for each model, their final results were compared in terms of accuracy for predicting drug solubility. The ET model had the best result with a score of 0.9999 on the R2 criterion. Random forests with 0.978 and KNN with 0.972 also had acceptable regression results. Finally, the trained model was used to display and evaluate the effect of input parameters like pressure and temperature on drug solubility to understand the process.

Abstract 3140: Computational modeling of stromal-immune cell interactions in triple-negative breast cancer

Abstract Breast cancer is the most common cancer type in women worldwide, and it represents the second most common cause of cancer deaths. Compared with other breast cancer subtypes, triple-negative breast cancer (TNBC) has the worst prognosis. The tumor microenvironment (TME) of TNBC is a complex ecosystem, characterized by the dynamic interactions between stromal, immune, extracellular matrices (ECM), and cancer cells. Understanding the fibroblasts as a heterogeneous population with pleiotropic actions on TME, mediated by ECM formation and interactions with immune cells, is necessary to develop approaches to interfere with the loss of ECM homeostasis, promote immunity, and improve the outcome of concomitant therapeutic approaches in TNBC and other cancer types. Our recent studies suggested distinct fibroblast sub cell types of varied biosynthesis of ECM components in the TME of TNBC, which have significant associations with the infiltration and activity of immune cells. In this study, we developed advanced computational methods to characterize the role of fibroblast sub cell types, their functional variations, and stromal-immune cell interactions in TNBC, by integrative analysis of tissue, single-cell, and spatial multi-omics data. We hypothesized that the genes involved in sub cell types and functional variations of fibroblast cells may form distinct data structures in omics data, which are identifiable by carefully designed pattern recognition methods. We also hypothesized that different subtypes and functional activities of fibroblast cells lead to varied ECM formations, metabolic shifts, and affect the infiltration and functions of immune cells, which could be identified as the spatial regions with distinct expression changes of stromal and immune marker genes.We first reconstructed TNBC TME context-specific gene sets of fibroblast sub cell types and functions. We assemble a series of computational methods including tissue data deconvolution, functional module detection on scRNA-seq data, and metabolic flux analysis into an integrative data analysis framework to characterize fibroblast cells and their functions in the TME of TNBC and reconstruct context-specific pathways of fibroblast sub cell types and functions. We further detected spatially dependent ECM formation and stromal-immune cell interactions in TNBC by using spatial resolved transcriptomics data. We observed spatially dependent variations of (1) sub cell type and cell type-specific functions, and (2) stromal-immune cell interactions, in the TME of TNBC. Citation Format: Yuhui Wei, Haiqi Zhu, Xiao Wang, Xinyu Zhou, Jia Wang, Tingbo Guo, Pengtao Dang, Sha Cao, Chi Zhang. Computational modeling of stromal-immune cell interactions in triple-negative breast cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 3140.

Towards a precise understanding of social entrepreneurship: An integrated bibliometric–machine learning based review and research agenda

This paper focuses on building a more precise and comprehensive understanding of the state of social entrepreneurship (SE) research by using an integrated bibliometric and unsupervised machine learning approach. Bibliometric analysis, along with Latent Dirichlet Allocation (LDA) for topic modeling enables us to identify key trends and themes in the SE domain. This approach is superior to tools and methods used in the past, which primarily employed systematic literature reviews and bibliometric analysis. While systematic manual literature reviews become impractical as the literature grows, bibliometric analysis focuses on the most cited articles, ignoring recent influential work, suffering from citation biases, and giving more weight to impact over thematic discovery. The methodology used by us overcomes these issues by first extracting large amounts of information through advanced computational methods and then using unsupervised machine learning to discover the latent themes and topics in this large collection of publications. This research uses the Scopus and Web of Science (WoS) databases to extract corpora of 3844 texts (titles, abstracts, and keywords) from published research on SE. We decipher the key trends in the literature and segregate them into three broad categories – individual attributes and motivation, organizational actions, and institutional conditions and development, with 21 sub-topics to enhance the understanding of this field of inquiry. This study is the first in the entrepreneurship domain to use this integrated approach to review the literature, and the findings lay the groundwork for future research.

Machines on Genes through the Computational Microscope.

Macromolecular machines acting on genes are at the core of life's fundamental processes, including DNA replication and repair, gene transcription and regulation, chromatin packaging, RNA splicing, and genome editing. Here, we report the increasing role of computational biophysics in characterizing the mechanisms of "machines on genes", focusing on innovative applications of computational methods and their integration with structural and biophysical experiments. We showcase how state-of-the-art computational methods, including classical and ab initio molecular dynamics to enhanced sampling techniques, and coarse-grained approaches are used for understanding and exploring gene machines for real-world applications. As this review unfolds, advanced computational methods describe the biophysical function that is unseen through experimental techniques, accomplishing the power of the "computational microscope", an expression coined by Klaus Schulten to highlight the extraordinary capability of computer simulations. Pushing the frontiers of computational biophysics toward a pragmatic representation of large multimegadalton biomolecular complexes is instrumental in bridging the gap between experimentally obtained macroscopic observables and the molecular principles playing at the microscopic level. This understanding will help harness molecular machines for medical, pharmaceutical, and biotechnological purposes.

Potential risk resulting from the influence of static magnetic field upon living organisms. Numerically simulated effects of the static magnetic field upon fatty acids and their glycerides

Background: We attempt to recognise the effects of static magnetic field (SMF) of varying flux density on flora and fauna. For this purpose, the influence of static magnetic field is studied for molecules of octadecanoic (stearic), cis-octadec-9-enoic (oleic), cis,cis-octadec-9,12-dienoic (linoleic), all cis-octadec-6,9.12-trienoic (linolenic), trans-octadec-9-enoic – (elaidic), cis-octadec-11-enoic (vaccenic) and all trans-octadec-6,9,12-trienoic (trans-linolenic) acids as well as 1- and 2-caproyl monoglycerides, 1,2- and 1,3-caproyl diglycerides and 1,2,3-caproyl triglyceride. In such a manner we attempt to develop an understanding of the interactions of living cells with SMF on a molecular level. Methods: Computations of the effect of real SMF 0.0, 0.1, 1, 10 and 100 AMFU (Arbitrary Magnetic Field Unit; here 1AMFU > 1000 T) flux density were performed in silico (computer vacuum), involving advanced computational methods. Results: SMF polarises molecules depending on applied flux density It neither ionises nor breaks valence bonds at 0.1 and 1 AMFU. In some molecules under consideration flux density of 10 and 100AMFU some C-H and C-C bonds were broken. Some irregularities were observed in the changes of positive and negative charge densities and bond lengths against increasing flux density. They provide evidence that molecules slightly change their initially fixed positions with respect to the force lines of the magnetic field. The length of some bonds and bond angles change with an increase in the applied flux density providing, in some cases, polar interactions between atoms through space. Conclusions: SMF destabilizes lipid acids and caproyl glycerides irregularly against increasing flux density. That irregularity results from the ability of those molecules to twist out of the initially established SMF plain and squeeze molecules around some bonds. In some molecules SMF flux density of 10 AMFU and above breaks some valence bonds and only in case of elaidic acid the trans-cis conversion is observed. Depending on the structure and applied flux density SMF either stimulates or inhibits metabolic processes of the lipids under study.

Digital approaches for predicting posttraumatic stress and resilience: promises, challenges, and future directions

AbstractDigital technologies and advances in computational methods have become key drivers of innovation in many medical fields. In precision psychiatry, accurate and reliable measures of mental health are critical for informing patient care and clinical research. There has been growing concern over the limitations of traditional mental health assessments that are typically grounded in nosology defined by the DSM and are based on interviewer-led assessments or patient self-report questionnaires. Whereas such gold-standard clinical assessments can be cost-prohibitive, insensitive to change, and prone to subjective biases, the use of digital technologies provides an opportunity to improve the practical feasibility as well as the inter-rater and test-retest reliability of repeated mental health assessments. The key promise of this approach is to unlock the clinical potential of digital technologies in ways that foster research of high clinical relevance and impact on clinical care. I will discuss these promises and challenges for the future use of machine learning approaches for predicting and monitoring post-traumatic stress and resilience.Disclosure of InterestNone Declared

COMPUTATIONAL INTELLIGENCE METHODS TO PATIENTS STRATIFICATION IN THE MEDICAL MONITORING SYSTEMS

Context. In modern medical practice the automation and information technologies are increasingly being implemented for diagnosing diseases, monitoring the condition of patients, determining the treatment program, etc. Therefore, the development of new and improvement of existing methods of the patient stratification in the medical monitoring systems is timely and necessary.
 Objective. The goal of intelligent diagnostics of patient’s state in the medical monitoring systems – reducing the likelihood of adverse states based on the choice of an individual treatment program:
 − reducing the probability of incorrectly determining the state of the patients when monitoring patients;
 − obtaining stable effective estimates of unknown values of treatment actions for patients (corresponding to the found state);
 − the choice of a rational individual treatment program for the patients, identified on the basis of the forecasted state.
 Method. Proposed methodology, which includes the following computational intelligence methods to patient’s stratification in the medical monitoring systems:
 1) method of cluster analysis based on the agent-based approach – the determination of the possible number of patient’s states using controlled variables of state;
 2) method of robust metamodels development by means artificial neuron networks under a priori data uncertainty (only accuracy of measurements is known) in the monitoring data: a) a multidimensional logistic regression model in the form of analytical dependences of the posterior probabilities of different states of the patients on the control and controlled variables of state; b) a multidimensional diagnostic model in the form of analytical dependences of the objective functions (quality criteria of the patient’s state) on the control and controlled variables of state;
 3) method of estimating informativeness controlled variables of state at a priori data uncertainty;
 4) method of robust multidimensional models development for the patient’s state control under a priori data uncertainty in the monitoring data in the form of analytical dependencies predicted from the measured values of the control and controlled variables of state in the monitoring process;
 5) method of reducing the controlled state variables space dimension based on the analysis of the variables informativeness of the robust multidimensional models for the patient’s state control;
 6) method of patient’s states determination based on the classification problem solution with the values of the control and forecasted controlled variables of state with using the probabilistic neural networks;
 7) method of synthesis the rational individual patient’s treatment program in the medical monitoring system, for the state identified on the basis of the forecast.
 Proposed the structure of the model for choosing the rational individual patient’s treatment program based on IT Data Stream Mining, which implements the «Big Data for Better Outcomes» concept.
 Results. The developed advanced computational intelligence methods for forecast states were used in choosing the tactics of treating patients, to forecast treatment complications and assess the patient’s curability before and during special treatment.
 Conclusions. Experience in the implementation of “Big Data for Better Outcomes” concept for the solution of the problem of computational models for new patient stratification strategies is presented. Advanced methodology, computational methods for a patient stratification in the medical monitoring systems and applied information technology realizing them have been developed. The developed methods for forecast states can be used in choosing the tactics of treating patients, to forecast treatment complications and assess the patient’s curability before and during special treatment.

Nonlinear dynamic thermal-mechanical behaviors of framed structures under temperature variations

An abnormal change in dynamic characteristics is considered an indicator of structural damage and can help the optimal design of structures suffering from dynamic loads, but these characteristics are also affected by ambient temperature. Therefore, a novel advanced computational method based on the fiber-based approach is proposed for the first time to predict the nonlinear dynamic behaviors of frames under environmental temperature changes with linear and nonlinear profiles by using Fortran programming language. The cross-section will be divided into a matrix of fibers, and by assigning the different temperatures to each fiber, all linear and nonlinear temperature gradients in both vertical and horizontal directions will be operated. The material nonlinearity is traced by the distributed plasticity model using the same mesh as the fiber-based approach whereas the effects of geometric nonlinearity due to member curvature and member chord rotation are considered by using the stability functions and a geometric matrix, respectively. A sequential nonlinear thermal incremental-iterative solution scheme based on the Newton-Raphson algorithm and Newmark-β method is also developed to address the nonlinear dynamic problems due to temperature variations. The accuracy and computational performance of the proposed method are validated by comparing the obtained results with those from experiments and the Abaqus. Results obtained show that the proposed method is exact for the nonlinear inelastic dynamic thermo-mechanical analysis of framed structures. It would offer a tool for thermal-structural design practice instead of using time and cost-consuming commercial software.

Multi-objective optimal design of mechanical metafilters based on principal component analysis

In this paper, an advanced computational method is proposed, whose aim is to obtain an approximately optimal design of a particular class of acoustic metamaterials, by means of a novel combination of multi-objective optimization and dimensionality reduction. Metamaterials are modeled as beam lattices with internal local resonators coupled with the microstructure through a viscoelastic phase. The dynamics is governed by a set of integro-differential equations, that are transformed into the Z-Laplace space in order to derive an eigenproblem whose solution provides the dispersion relation of the free in-plane propagating Bloch waves. A multi-objective optimization problem is stated, whose aim is to achieve the largest multiplicative trade-off between the bandwidth of the first stop band and the one of the successive pass band in the metamaterial frequency spectrum. Motivated by the multi-dimensionality of the design parameters space, the goal above is achieved by integrating numerical optimization with machine learning. Specifically, the problem is solved by combining a sequential linear programming algorithm with principal component analysis, exploited as a data dimensionality reduction technique and applied to a properly sampled field of gradient directions, with the aim to perform an optimized sensitivity analysis. This represents an original way of applying principal component analysis in connection with multi-objective optimization. Successful performances of the proposed optimization method and its computational savings are demonstrated.

Special Section Guest Editorial: Advances in Computational Methods for Optical System Forward- and Inverse-Design

Special Section Guest Editorial: Advances in Computational Methods for Optical System Forward- and Inverse-Design

Narrative review of radiomics for classifying pulmonary nodules and potential impact on lung cancer screening

: Lung cancer screening has proven to be a useful tool for identifying early stage lung cancers, however, the overall accuracy can sometimes lead to false positive and negatives that have potential adverse effects on patient outcomes. Advancement in computational methods have allowed for quantification of pulmonary nodule imaging features, referred to as radiomics, which have the potential to increase lung cancer screening accuracy and improve patient management. The initial part of this review covers common radiomic features and the challenges in deriving them. The second part of this review systematically evaluates literature relating to radiomics and lung cancer finding articles in areas that might have the potential to change management in lung cancer screening. Pertinent literature included initial nodule classification as benign or malignant, classifying subsolid nodules as invasive or noninvasive, and prediction of tumor recurrence after surgical resection. The reviewed articles evaluating use of radiomics are mostly limited due to small sample sizes and lack of a validation cohort. These studies show potential for radiomic features to improve pulmonary nodule classification and change the way patients are managed, however, comparison between studies is limited due to variabilities in the way these features are derived. To make these features useful will require further research and standardization of the workflows that derive these features.

Computational control of gene expression in individual yeast using reactive microscopy.

Biotechnology is rapidly improving, largely due to our ability to manipulate genetic material and efficiently measure biological processes. This talk will describe our advances in computational methods and experimental platforms to probe and control stochastic biological processes within individual cells under a microscope. I will describe a novel optogenetics-based approach in which cells are segmented and controlled in real time. An optogenetically regulated gene in each yeast cell under the microscope is modeled using the Chemical Master Equation and controlled independently using a digital micromirror device to send light to only that cell. I will briefly describe the open source python-based software we developed, called MicroMator, that enabled this work, and show how we used it for optogenetically controlled recombination and adaptive illumination of a photobleaching fluorophore. This work brings together stochastic modeling of gene regulation, machine learning based image processing, and automated microscopy to provide methods for understanding basic cellular behavior.

2D-IR spectroscopy of proteins in H2O-A Perspective.

The form of the amide I infrared absorption band provides a sensitive probe of the secondary structure and dynamics of proteins in the solution phase. However, the frequency coincidence of the amide I band with the bending vibrational mode of H2O has necessitated the widespread use of deuterated solvents. Recently, it has been demonstrated that ultrafast 2D-IR spectroscopy allows the detection of the protein amide I band in H2O-based fluids, meaning that IR methods can now be applied to study proteins in physiologically relevant solvents. In this perspective, we describe the basis of the 2D-IR method for observing the protein amide I band in H2O and show how this development has the potential to impact areas ranging from our fundamental appreciation of protein structural dynamics to new applications for 2D-IR spectroscopy in the analytical and biomedical sciences. In addition, we discuss how the spectral response of water, rather than being a hindrance, now provides a basis for new approaches to data pre-processing, standardization of 2D-IR data collection, and signal quantification. Ultimately, we visualize a direction of travel toward the creation of 2D-IR spectral libraries that can be linked to advanced computational methods for use in high-throughput protein screening and disease diagnosis.

ADVANCED CLUSTERING OF ARCHITECTURAL GEOMETRIC ORNAMENTS USING SMALL SCALE MACHINE LEARNING, CASE STUDY OF ILKHANID GEOMETRIC PATTERNS

Abstract. Classification is an essential step for architectural historians for better understanding and typology of cultural heritage. This research aims to automatically cluster geometric ornaments of the Ilkhanid period in Iran through using machine learning It examines the application of advanced computer science tools and methods in analysis of architectural heritage by searching the possibility of clustering ornament images with machine learning into different clusters. After examining case studies of mosques, tombs or other buildings in Iran from the Ilkhanid period (1256–1335 CE), 231 images from 36 existing buildings were chosen, and after editing images, an inventory was created based on characterization of each ornament (image) containing: Buildings’ Name, Region, Construction Date, Functionality, Repetition of Motifs, Material, Ornamental Types, Design Complexity, Dominant Colour, Star-Number, Geometric Shape, Geometric Lines, and Geometric Pieces. Next, these images were analysed with small-scale machine learning with the help of the visual programming toolbox Orange (http://orange.biolab.si). The results containing image groups (machine clustered images) were tested with the characterization table of ornaments, and groups of ornaments that represents a style is introduced.

Conformational Features and Hydration Dynamics of Proteins in Cosolvents: A Perspective from Computational Approaches.

The importance of solvent in stabilizing protein structures has long been recognized. Water is the common solvent for proteins, and hydration is elemental in governing protein stability, flexibility, and function through various interactions. The addition of small organic molecules known as cosolvents may deploy stabilization (folding) or destabilization (unfolding) effects on native protein conformations. Despite exhaustive literature, the molecular mechanism by which cosolvents regulate protein conformations and dynamics is controversial. Specifically, the cosolvent behavior has been unpredictable with the nature and concentrations that lead to protein stabilizing/destabilizing effects as it changes in water content near the vicinity of proteins. With the massive development of computational resources, advancement of computational methods, and the availability of numerous experimental techniques, various theoretical and computational studies of proteins in a mixture of solvents have been instigated. The growing interest in such studies has been to unravel the underlying mechanism of protein folding and cosolvent/solvent-protein interactions that have significant implications in biomedical and biotechnological applications. In this mini-review, apart from the brief overview of important theories and force-field model-based cosolvent effects on proteins, we present the current state of knowledge and recent advances in the field to describe cosolvent-guided conformational features of proteins and hydration dynamics from computational approaches. The mini-review further explains the mechanistic details of protein stability in various popularly used cosolvents, including limitations of present studies and future outlooks. The counteracting effects of cosolvent on the proteins in the mixture of stabilizing and destabilizing cosolvents are also presented and discussed.

Activity‐Based Protein Profiling – Enabling Phenotyping of Host‐Associated and Environmental Microbiomes

AbstractHost‐associated and environmental microbiomes play central roles in human health, biogeochemical cycling, response to ecosystem change, and agriculture. Scientific approaches that can reveal the functional activities that contribute to observed phenotypes are needed in microbiome research. Broad characterization of the functional activity of microbes within microbiomes is currently hampered by approaches that rely on inference of function from metagenomes or indirect measurements. Activity‐based protein profiling is uniquely positioned to overcome these challenges and reveal the protein‐level mechanisms associated with microbiome phenotypes. In this review we describe the progress made to date using ABPP in gut microbiome, plant‐microbe interaction, and soil microbiome research, and suggest how ABPP data can be coupled with advanced computational methods to enable phenotype prediction and bioengineering of microbiomes for applied purposes.