Preface: Recent Advances in Computational Methods (ICCM 2024) – Part 1

Biosensors are analytical tools with a great application in healthcare, food quality control, and environmental monitoring. They are of considerable interest to be designed by using cost-effective and efficient approaches. Designing biosensors with improved functionality or application in new target detection has been converted to a fast-growing field of biomedicine and biotechnology branches. Experimental efforts have led to valuable successes in the field of biosensor design; however, some deficiencies restrict their utilization for this purpose. Computational design of biosensors is introduced as a promising key to eliminate the gap. A set of reliable structure prediction of the biosensor segments, their stability, and accurate descriptors of molecular interactions are required to computationally design biosensors. In this review, we provide a comprehensive insight into the progress of computational methods to guide the design and development of biosensors, including molecular dynamicssimulation, quantum mechanicscalculations, molecular docking, virtual screening, and a combination of them as the hybrid methodologies. By relying on the recent advances in the computational methods, an opportunity emerged for them to be complementary or an alternative to the experimental methods in the field of biosensor design.

Allostery is a universal, biological phenomenon in which orthosteric sites are fine‐tuned by topologically distal allosteric sites triggered by perturbations, such as ligand binding, residue mutations, or post‐translational modifications. Allosteric regulation is implicated in a variety of physiological and pathological conditions and is thus emerging as a novel avenue for drug discovery. Allosteric drugs have traditionally been discovered by serendipity through large‐scale experimental screening. Recently, we have witnessed significant progress in biophysics, particularly in structural bioinformatics, which has facilitated the in‐depth characterization of allosteric effects and the accurate detection of allosteric residues and exosites. These advances improve our understanding of allosterism and promote allosteric drug discovery, thereby revolutionizing the shift from the traditional serendipitous route used to discover allosteric drugs to the updated path centered on rational structure‐based design. In this review, recent advances in computational methods applied to allosteric drug discovery are summarized. We comprehensively review these achievements along various levels of allosteric events, from the construction of allosteric databases to the identification and analysis of allosteric residues, signals, sites, and modulators. We expect to increase the awareness of the discovery of allosteric drugs using structure‐based computational methods.This article is categorized under:Structure and Mechanism > Computational Biochemistry and Biophysics

Recent Advances in Computational Methods for Nuclear Magnetic Resonance Data Processing

Genetic diseases are a wide group of diseases in which the etiopathogenesis is caused by or related to genetic factors. The role of genetics in the disease development can be more or less relevant depending on the specific characteristics of the disease, and a wide spectrum of complexity exists. Monogenic diseases, for example, are directly caused by defects in a specific gene whereas complex and polygenic diseases are generally caused by the interactions between multiple genes or between genetic and environmental factors. To the last category belong many forms of cancer, an uncontrolled growth of cells with alterations of the genetic materials. In the last decade, a large amount of experimental data has become available, so the identification of strategies to process and, most importantly, interpret them is crucial. The massive volume of data, both in terms of quantity and of dimensionality, and their heterogeneity and low signal-to-noise ratio are just some of the most obvious challenges that they present. To give an example, single nucleotide DNA mutations are one of the most common factors analysed in relation to the development of a genetic disease. However, this sometimes translates into dealing with millions of variants measured across thousands of individuals, where only a handful are informative. In fact, other more complex factors, such as gene expression, could play a significant role. The aim of this special issue is to review the recent advances in computational methods concerned with genetic diseases. The issue received sixteen submissions; each one was referred by at least two international reviewers that we warmly thank for their time. Six papers have been accepted for the publication. “A New Approach for Mining Order-Preserving Submatrices Based on all Common Subsequences” by Y. Xue et al. proposes, in the context of gene expression data, a pattern-based subspace clustering or OPSM (order-preserving submatrix model), based on frequent sequential pattern. The approach has been experimentally proven to be able to discover the biological significant OPSMs and deep OPSMs exhaustively. “Evolutionary Influenced Interaction Pattern as Indicator for the Investigation of Natural Variants Causing Nephrogenic Diabetes Insipidus” by S. Grunert and D. Labudde is devoted to the application of a high-throughput analysis method based on motif conservation among proteins of the same protein family for analysis of interacting sequences. This investigation can help to analyze the pathogenic impact of mutations causing alterations in interacting regions of a protein. This analysis has been applied on membrane proteins, in particular to the aquaporin 2 whose mutants are involved in nephrogenic diabetes insipidus. “Unified Modeling of Familial Mediterranean Fever and Cryopyrin Associated Periodic Syndromes” by Y. Bozkurt et al. describes a unifying dynamical model for Familial Mediterranean Fever (FMF) and Cryopyrin Associated Periodic Syndromes (CAPS) in the form of coupled nonlinear ordinary differential equations. The authors perform a comprehensive bifurcation analysis of the model and show that it exhibits three modes, capturing the healthy, FMF, and CAPS cases. They present extensive simulation results for the model that match clinical observations. “Enhancing the Lasso Approach for Developing a Survival Prediction Model Based on Gene Expression Data” by S. Kaneko et al. presents a novel improvement to the lasso approach, one of the most widely used method to correlate gene expression data with cancer patients' survival. This new algorithm significantly increases the ability to identify “true positives” and its validity is shown on both simulated and real data. “Statistical and Computational Methods for Genetic Diseases: An Overview” by Francesco Camastra, Maria Donata Di Taranto, and Antonino Staiano gives a survey of statistical and computational methods used to analyse the pathogenic role of sequence variants as well as to identify genetic markers of complex diseases by association studies, meta-analysis, and expression studies. “Optimization and Corroboration of the Regulatory Pathway of p42.3 Protein in the Pathogenesis of Gastric Carcinoma” by Y. Hao et al. provides important research directions for exploring the mechanism of action of p42.3 protein in gastric cancer. Through a Bayesian network model, the potential important role of p42.3 is verified by both theoretical analysis and preliminary test. We hope that the readers of this journal will find in the issue interesting papers and that this can encourage and foster further research on computational methods for genetic diseases. Francesco Camastra Roberto Amato Maria Donata Di Taranto Antonino Staiano

Photocatalysis, particularly in energy conversion and environmental remediation, has become a significant technology due to its ability to utilise solar energy to degrade pollutants and produce clean energy. At the core of developing effective photocatalysts are quantum chemistry methods, most notably Density Functional Theory (DFT), which can be employed to simulate electronic structures and predict catalytic behaviour. This review paper discusses the theoretical methods used in photocatalysis, focusing on DFT and its developments, including hybrid functionals, meta-GGA, and their range-separated hybrid models. Furthermore, we discuss multi-configurational and perturbation theory methods, which are used for systems with strong electron correlations, and integrating DFT with machine learning to accelerate the discovery of new photocatalytic materials. The paper focuses on DFT's role in synthesising new materials, notably metal-organic frameworks (MOFS). It presents their applications in water-splitting photocatalysis, CO2 reduction, and the degradation of organic pollutants. Finally, we review recent developments in computational methods used to model the mechanisms and reactions of photocatalysis, focusing on the need to optimise the light-matter interface. Despite the immense promise, challenges persist in accurately modelling complex photocatalysis systems, necessitating ongoing advances in computational methods. The advancement of photocatalysis will depend on aligning theory and experiment and refining computational models to optimise the efficiency and scalability of catalysis processes.

Lithium–sulfur (Li–S) batteries, as one of the most promising “post‐Li‐ion” energy storage devices, encounter several intrinsic challenges: polysulfide dissolution and shuttle effect, poor sulfur utilization, lithiation‐induced sulfur expansion, and lithium dendritic growth. These challenges must be resolved, and the associated mechanisms must be completely understood before the practical applications of Li–S batteries. Despite significant progress in enhancing battery capacities, experimental studies on the working mechanisms of Li–S batteries remain challenging. Alternatively, computational methods are proven useful for understanding Li–S electrochemistry and designing high‐performance Li–S batteries. This review presents recent advances in computational methods (density functional theory, molecular dynamics simulations, and finite element analysis) for Li–S batteries, compares their advantages, and summarizes their favorable applications in addressing the challenges of Li–S batteries. Computational methods should find more applications in the development of Li–S batteries.

Recent focus in non-SELEX-computational approach for de novo aptamer design: A mini review.

This review focuses primarily on what is probably the most important development in computational methods in recent years, namely, the so-called Accelerated Lambda Iteration (ALI) methods. The roots and development of these methods are traced, and the major variants are discussed.

This paper provides a comprehensive review of recent advancements in computational methods for modeling, simulation, and optimization of complex systems in materials engineering, mechanical engineering, and energy systems. We identified key trends and highlighted the integration of artificial intelligence (AI) with traditional computational methods. Some of the cited works were previously published within the topic: "Computational Methods: Modeling, Simulations, and Optimization of Complex Systems"; thus, this article compiles the latest reports from this field. The work presents various contemporary applications of advanced computational algorithms, including AI methods. It also introduces proposals for novel strategies in materials production and optimization methods within the energy systems domain. It is essential to optimize the properties of materials used in energy. Our findings demonstrate significant improvements in accuracy and efficiency, offering valuable insights for researchers and practitioners. This review contributes to the field by synthesizing state-of-the-art developments and suggesting directions for future research, underscoring the critical role of these methods in advancing engineering and technological solutions.

Recent advances in predicting lncRNA–disease associations based on computational methods

Carbon nanotubes (CNTs), as a promising nanomaterial with broad applications across various fields, are continuously attracting significant research attention. Despite substantial progress in understanding their growth mechanisms, synthesis methods, and post-processing techniques, two major goals remain challenging: achieving property-targeted growth and efficient mass production. Recent advancements in computational methods driven by increased computational resources, the development of platforms, and the refinement of theoretical models, have significantly deepened our understanding of the mechanisms underlying CNT growth. This review aims to comprehensively examine the latest computational techniques that shed light on various aspects of CNT synthesis. The first part of this review focuses on progress in computational methods. Beginning with atomistic simulation approaches, we introduce the fundamentals and advancements in density functional theory (DFT), molecular dynamics (MD) simulations, and kinetic Monte Carlo (kMC) simulations. We discuss the applicability and limitations of each method in studying mechanisms of CNT growth. Then, the focus shifts to multiscale modeling approaches, where we demonstrate the coupling of atomic-scale simulations with reactor-scale multiphase flow models. Given that CNT growth inherently spans multiple temporal and spatial scales, the development and application of multiscale modeling techniques are poised to become a central focus of future computational research in this field. Furthermore, this review emphasizes the growing role played by machine learning in CNT growth research. Compared with traditional physics-based simulation methods, data-driven machine learning approaches have rapidly emerged in recent years, revolutionizing research paradigms from molecular simulation to experimental design. In the second part of this review, we highlight the latest advancements in CNT growth mechanisms and synthesis methods achieved through computational techniques. These include novel findings across fundamental growth stages, i.e., from nucleation to elongation and ultimately termination. We also examine the dynamic behaviors of catalyst nanoparticles and chirality-controlled growth processes, emphasizing how these insights contribute to advancing the field. Finally, in the concluding section, we propose future directions for advancements of computational approaches toward deeper understanding of CNT growth mechanisms and better support of CNT manufacturing.

Large-scale CRISPR-Cas pooled screens have shown great promise to investigate functional links between genotype and phenotype at the genome-wide scale. In addition to technological advancement, there is a need to develop computational methods to analyze the large datasets obtained from high-throughput CRISPR screens. Many computational methods have been developed to identify reliable gene hits from various screens. In this review, we provide an overview of the technology development of CRISPR screening platforms, with a focus on recent advances in computational methods to identify and model gene effects using CRISPR screen datasets. We also discuss existing challenges and opportunities for future computational methods development.

Fu-SulfPred: Identification of Protein S-sulfenylation Sites by Fusing Forests via Chou’s General PseAAC

NAD(P)H:quinone oxidoreductase 1 (NQO1) has been shown to be a potential therapeutic target for various human diseases, such as cancer and neurodegenerative disorders. Recent advances in computational methods, especially network-based methods, have made it possible to identify novel compounds for a target with high efficiency and low cost. In this study, we designed a workflow combining network-based methods and identification of privileged substructures to discover new compounds targeting NQO1 from a natural product library. According to the prediction results, we purchased 56 compounds for experimental validation. Without the assistance of privileged substructures, 31 compounds (31/56 = 55.4%) showed IC50 < 100 μM, and 11 compounds (11/56 = 19.6%) showed IC50 < 10 μM. With the assistance of privileged substructures, the two success rates were increased to 61.8 and 26.5%, respectively. Seven natural products further showed inhibitory activity on NQO1 at the cellular level, which may serve as lead compounds for further development. Moreover, network analysis revealed that osthole may exert anticancer effects against multiple cancer types by inhibiting not only carbonic anhydrases IX and XII but also NQO1. Our workflow and computational methods can be easily applied in other targets and become useful tools in drug discovery and development.

Recent Advances in Computational Methods and Clinical Applications for Spine Imaging