AIMOES: Archive information assisted multi-objective evolutionary strategy for ab initio protein structure prediction

Adoption of an improved PSO to explore a compound multi-objective energy function in protein structure prediction

Most computational protein structure prediction methods are designed for either template based or template-free (ab initio) structure prediction. The approaches that integrate the prediction capabilities of both template-based modeling and template-free modeling are needed to synergistically combine the two kinds of methods to improve protein structure prediction. In this work, we develop a new method to integrate several protein structure prediction methods including our template-based MULTICOM server, our ab initio contact-based protein structure prediction method CONFOLD, our multi-template-based model generation tool MTMG, and locally installed external Rosetta, I-TASSER and RaptorX protein structure prediction tools to improve protein structure prediction of a fullspectrum difficulty ranging from easy, to medium and to hard. Our method participated in the 11(th) community-wide Critical Assessment of Techniques for Protein Structure Prediction (CASP11) in 2014 as MULTICOM-NOVEL server. It was ranked among top 10 methods for protein tertiary structure prediction according to the official CASP11 assessment, which demonstrates that integrating complementary modeling methods is useful for advancing protein structure prediction.

Objective:This research aims to study the target specificity of selective bioactive compounds in complexing with the human angiotensin-converting enzyme (hACE2) receptor to impede the severe acute respiratory syndrome coronavirus 2 influx mechanism resulting in cardiac injury and depending on the receptor’s active site properties and quantum tunneling.Materials and Methods:A library of 120 phytochemical ligands was prepared, from which 5 were selected considering their absorption, distribution, metabolism, and excretion (ADMET) and quantitative structure–activity relationship (QSAR) profiles. The protein active sites and belonging quantum tunnels were defined to conduct supramolecular docking of the aforementioned ligands. The hydrogen bond formation and hydrophobic interactions between the ligand–receptor complexes were studied following the molecular docking steps. A comprehensive molecular dynamic simulation (MDS) was conducted for each of the ligand–receptor complexes to figure out the values – root mean square deviation (RMSD) (Å), root mean square fluctuation (RMSF) (Å), H-bonds, Cα, solvent accessible surface area (SASA) (Å2), molecular surface area (MolSA) (Å2), Rg (nm), and polar surface area (PSA) (Å). Finally, computational programming and algorithms were used to interpret the dynamic simulation outputs into their graphical quantitative forms.Results:ADMET and QSAR profiles revealed that the most active candidates from the library to be used were apigenin, isovitexin, piperolactam A, and quercetin as test ligands, whereas serpentine as the control. Based on the binding affinities of supramolecular docking and the parameters of molecular dynamic simulation, the strength of the test ligands can be classified as isovitexin > quercetin > piperolactam A > apigenin when complexed with the hACE2 receptor. Surprisingly, serpentine showed lower affinity (−8.6 kcal/mol) than that of isovitexin (−9.9 kcal/mol) and quercetin (−8.9 kcal/mol). The MDS analysis revealed all ligands except isovitexin having a value lower than 2.5 Ǻ. All the test ligands exhibited acceptable fluctuation ranges of RMSD (Å), RMSF (Å), H-bonds, Cα, SASA (Å2), MolSA (Å2), Rg (nm), and PSA (Å) values.Conclusion:Considering each of the parameters of molecular optimization, docking, and dynamic simulation interventions, all of the test ligands can be suggested as potential targeted drugs in blocking the hACE2 receptor.

Predicting the 3D native conformation of a protein given the amino acid sequence is known as protein structure prediction (PSP) problem and is one of the greatest challenges of computational biology. The current work uses a real valued Genetic Algorithm (GA), a powerful variate of GA to simulate the PSP problem. This algorithm consists of basic evolutionary operators and a fitness vector. The fitness vector is designed by combining a set of knowledge based biophysical filters viz. persistence length, radius of gyration, packing fraction, hydrophobicity ratio and irregularity index of phi and psi. This vector converts all these biophysical measures into a real value by using specific weights or factors. The algorithm has been validated on six known globular proteins, with their length varying from 17-61 residues and total number of helices and strands in the range of 2-4. For all the test protein the algorithm converges rapidly and the converged structure shows a backbone RMSD (root mean square deviation) of 3-6A as compared to the native structure.

Protein structure prediction is one of the most challenging problems in computational biology. Given a protein's amino acid sequence, a simplified version of the problem is to find an on-lattice self-avoiding walk that minimizes the interaction energy among the amino acids. In this paper, we present a memory-based local search method for the simplified problem using Hydrophobic-Polar energy model and Face Centered Cubic lattice. By memorizing local minima and then avoiding their neighbohood, our approach significantly improves the state-of-the-art local search method for protein structure prediction on a set of standard benchmark proteins.

Protein complex structure prediction is an important problem in computational biology. While significant progress has been made for protein monomers, accurate evaluation of protein complexes remains challenging. Existing assessment methods in CASP, lack dedicated metrics for evaluating complexes. DockQ, a widely used metric, has some limitations. In this study, we propose a novel metric called BDM (Based on Distance difference Matrix) for assessing protein complex prediction structures. Our approach utilizes a distance difference matrix derived from comparing real and predicted protein structures, establishing a linear correlation with Root Mean Square Deviation (RMSD). BDM overcomes limitations associated with receptor-ligand differentiation and eliminates the requirement for structure alignment, making it a more effective and efficient metric. Evaluation of BDM using CASP14 and CASP15 test sets demonstrates superior performance compared to the official CASP scoring. BDM provides accurate and reasonable assessments of predicted protein complexes, wide adoption of BDM has the potential to advance protein complex structure prediction and facilitate related researches across scientific domains. Code is available at http://mialab.ruc.edu.cn/BDMServer/ .

To solve protein structure prediction (PSP) problems computationally, a plethora of template-based methods exist. However, there are very few ab initio models for PSP. Template-based modeling relies on the existing structures and therefore is not effective for non-homologous sequence-based structure prediction. Thus, ab initio modeling is indispensable in such cases, even though it is a challenging optimization problem. To cope, we utilize an effective energy function (called 3DIGARS) and an advanced search algorithm (called KGA) based ab initio PSP, called 3DIGARS-PSP. To address critical search, the proposed genetic algorithm deploys two effective operators: angle rotation and segment translation. Further, propensities of torsion angle and secondary structure distribution have been utilized to guide the conformation search. Crucial features, such as sequence-specific accessibility, hydrophobic-hydrophilic properties and torsion angles of protein residues are mined to formulate an optimized energy function, which is then combined with the advanced sampling algorithm to explore critical conformational space. Consequently, 3DIGARS-PSP performed well compared to the state-of-the-art method for a set of low TMscore models from CASP data.

Proteins are essential parts of our life and participate in virtually every process within a cell. The understanding of protein structures is vital to determine the function of a protein. Protein structure prediction (PSP) from amino acid sequence is one of the high focus problems in bioinformatics today. This is due to the fact that the biological function of the protein is determined by its three dimensional structure. Thus, protein structure prediction is a fundamental area of computational biology. Its importance is intensed by large amounts of sequence data coming from PDB (Protein Data Bank) and the fact that experimentally methods such as X-ray crystallography or Nuclear Magnetic Resonance (NMR)which are used to determining protein structures remains very expensive and time consuming. For minimizing the time, computational methods are used for protein folding and structure prediction problem. In this paper results of protein p53 are discussed.

BackgroundProtein structure comparison is one of the most important problems in computational biology and plays a key role in protein structure prediction, fold family classification, motif finding, phylogenetic tree reconstruction and protein docking.ResultsWe propose a novel method to compare the protein structures in an accurate and efficient manner. Such a method can be used to not only reveal divergent evolution, but also identify circular permutations and further detect active-sites. Specifically, we define the structure alignment as a multi-objective optimization problem, i.e., maximizing the number of aligned atoms and minimizing their root mean square distance. By controlling a single distance-related parameter, theoretically we can obtain a variety of optimal alignments corresponding to different optimal matching patterns, i.e., from a large matching portion to a small matching portion. The number of variables in our algorithm increases with the number of atoms of protein pairs in almost a linear manner. In addition to solid theoretical background, numerical experiments demonstrated significant improvement of our approach over the existing methods in terms of quality and efficiency. In particular, we show that divergent evolution, circular permutations and active-sites (or structural motifs) can be identified by our method. The software SAMO is available upon request from the authors, or from and .ConclusionA novel formulation is proposed to accurately align protein structures in the framework of multi-objective optimization, based on a sequence order-independent strategy. A fast and accurate algorithm based on the bipartite matching algorithm is developed by exploiting the special features. Convergence of computation is shown in experiments and is also theoretically proven.

One of the main research problems in Structural Bioinformatics is related to the prediction of three-dimensional structures (3-D) of polypeptides or proteins. The rate at which amino acid sequences are identified is increasing faster than the 3-D protein structure determination by experimental methods. Computational prediction methods have been developed during the last years, but the problem still remains challenging because of the complexity and high dimensionality of a protein conformational search space. In this article we present a hybrid genetic algorithm for the Protein Structure Prediction (PSP) Problem. A genetic algorithm is combined with a structured population, and it is hybridized with a path-relinking procedure that helps the algorithm to scape from local minima. We perform a set of experiments and show that the proposed hybrid genetic algorithm is effective in finding good quality solutions for the PSP Problem.

Exploring <i>Artocarpus heterophyllus</i> phytochemicals as novel mineralocorticoid receptor inhibitors: A computational approach to hypertension therapy

ABSTRACTObjectiveThis study aimed to investigate the accuracy of conventional and digital impression‐making and cast‐fabrication using coded healing abutments on an edentulous mandibular model under in vitro conditions.Materials and MethodsOur study investigated the accuracy of the On1 Concept (Nobel Biocare; Kloten, Switzerland) coded healing abutment system using conventional and digital workflows. The Conical Connection (CC) system (Nobel Biocare; Kloten, Switzerland) was used as the control group in both workflows. 10–10 open‐tray impressions and intraoral scans were made from the reference model with each system. Models built from intraoral scans were additively fabricated, and open‐tray impressions were poured with type‐4 dental stone. The prepared models were digitized using a desktop scanner with an accuracy of 4 μm (E4, 3Shape; Copenhagen, Denmark) and superimposed on the reference scan. Four linear distances and root mean square (RMS) deviations were measured using metrology software.ResultsFive dimensions were measured using signed and absolute deviations, resulting in nine outcomes. RMS and diagonal deviations provided the most insight into overall model deviations. Mean RMS deviations were: 58.30 (14.95) μm for CC_conv, 47.66 (13.04) μm for On1_conv, 204.97 (37.40) μm for CC_dig, and 136.64 (13.49) μm for On1_dig. Significant differences were found between On1_conv vs. CC_dig, On1_conv vs. On1_dig, and CC_conv vs. CC_dig. Mean linear deviations between the molar positions were: 24.49 (58.20) μm for CC_conv, 87.46 (106.70) μm for On1_conv, −104.76 (125.83) μm for CC_dig, and 140.64 (190.56) μm for On1_dig. Significant differences were observed between On1_conv vs. CC_dig and CC_dig vs. On1_dig.ConclusionsBased on the RMS deviations, the conventional method is significantly more accurate at both implant and platform levels in the case of an in vitro edentulous lower jaw model. The RMS deviations of the implant analogs are smaller on the platform level with both conventional and digital methods.

Protein structure prediction, a fundamental challenge emerging from the protein folding problem, forms the basis of modern computational biology. This field addresses the critical question of how the amino acids sequence determines its three-dimensional structure, a relationship critical to understanding biological function. Over the last four decades, methodologies have evolved from template-based modeling (TBM) and free modeling (FM) to advanced hybrid and end-to-end deep learning approaches. TBM explores sequence homology and threading to predict structures based on a known template, while FM applies physics-based principles to navigate the rugged energy landscape that governs protein folding, predicting de novo stable native conformations. Recent breakthrough methods in protein structure prediction include hybrid methods that integrate physics, bioinformatics, and machine learning, as well as end-to-end methods such as AlphaFold2 and RoseTTAFold, which have revolutionized the field by using neural networks to directly predict atomic coordinates from sequences, achieving near-experimental accuracy. Protein language models further advance the field by learning sequence-structure-function relationships directly from amino acid sequences, bypassing the need for multiple-sequence alignments. These innovations address the sequence-structure-function paradigm and find applications in drug discovery, enzyme engineering, and disease research. This chapter explores the principles, advances, and transformative impact of these methodologies on the structural biology field.

Molecular modeling has increasingly been used to investigate the structural dynamics, properties, and thermodynamics of biological systems. This study aims to evaluate the structural dynamics of gene-translated lipase structure from PLS 80, a novel bacterium previously isolated from undersea fumaroles, subjected to various temperatures. Prediction using SWISS-MODEL showed PLS 80 lipase had the highest homology (97,7%) with lipase 2DSN. The pair of PLS 80 lipase – 2DSN had the highest Global Model Quality Estimation (GMQE) and Qualitative Model Energy Analysis (QMEAN) values of 0.99 and 0.7, respectively. The lipase 2DSN was then used as the template. The temperature variation simulation was conducted using Assisted Model Building with Energy Refinement (AMBER) software at 300, 350, and 400 K for 50 ns. The parameters used for evaluation were Root Mean Square Deviation (RMSD), Root Mean Square Fluctuation (RMSF), the gyration radii (Rg), total energy, number of hydrogen bonds, and Solvent Accessible Surface Area (SASA). Although produced by a thermophile, the molecular dynamic simulation showed that the lipase had the lowest RMSD, Rg, and total energy when at 300 K. Higher temperatures increased the Rg and reduced the number of hydrogen bonds in the molecule. The RMSF values indicated that some residues were conformationally less rigid than other residues in the structure. The difference in RMSF values of some residues was significant when the temperature was increased, but not necessarily those with high RMSF values. The SASA values increased slightly when the temperature was alleviated by 100 K. However, the polar SASA value at 350 K was the lowest than the other temperatures. Visual evaluation of the lipase structure indicated that the b-sheet only shifted slightly at various temperatures. However, conformational changes were observed in the a-helix and turn/loop. At 350 K, the lipase had an additional b-sheet, while some turn/loop structures frayed to coil structures. These structural differences may explain the optimum lipase activity at around thermophilic temperature.

Computational screening and molecular dynamics simulation of disease associated nsSNPs in CENP-E