Journal articles: 'Algorithms- Protein'

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Hulianytskyi, Leonid, and Sergii Chornozhuk. "Genetic Algorithm with New Stochastic Greedy Crossover Operator for Protein Structure Folding Problem." Cybernetics and Computer Technologies, no. 2 (July 24, 2020): 19–29. http://dx.doi.org/10.34229/2707-451x.20.2.3.

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Introduction. The spatial protein structure folding is an important and actual problem in biology. Considering the mathematical model of the task, we can conclude that it comes down to the combinatorial optimization problem. Therefore, genetic and mimetic algorithms can be used to find a solution. The article proposes a genetic algorithm with a new greedy stochastic crossover operator, which differs from classical approaches with paying attention to qualities of possible ancestors. The purpose of the article is to describe a genetic algorithm with a new greedy stochastic crossover operator, reveal its advantages and disadvantages, compare the proposed algorithm with the best-known implementations of genetic and memetic algorithms for the spatial protein structure prediction, and make conclusions with future steps suggestion afterward. Result. The work of the proposed algorithm is compared with others on the basis of 10 known chains with a length of 48 first proposed in [13]. For each of the chain, a global minimum of free energy was already precalculated. The algorithm found 9 out of 10 spatial structures on which a global minimum of free energy is achieved and also demonstrated a better average value of solutions than the comparing algorithms. Conclusion. The quality of the genetic algorithm with the greedy stochastic crossover operator has been experimentally confirmed. Consequently, its further research is promising. For example, research on the selection of optimal algorithm parameters, improving the speed and quality of solutions found through alternative coding or parallelization. Also, it is worth testing the proposed algorithm on datasets with proteins of other lengths for further checks of the algorithm’s validity. Keywords: spatial protein structure, combinatorial optimization, genetic algorithms, crossover operator, stochasticity.

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Cavanaugh, David, and Krishnan Chittur. "A hydrophobic proclivity index for protein alignments." F1000Research 4 (October 21, 2015): 1097. http://dx.doi.org/10.12688/f1000research.6348.1.

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Sequence alignment algorithms are fundamental to modern bioinformatics. Sequence alignments are widely used in diverse applications such as phylogenetic analysis, database searches for related sequences to aid identification of unknown protein domain structures and classification of proteins and protein domains. Additionally, alignment algorithms are integral to the location of related proteins to secure understanding of unknown protein functions, to suggest the folded structure of proteins of unknown structure from location of homologous proteins and/or by locating homologous domains of known 3D structure. For proteins, alignment algorithms depend on information about amino acid substitutions that allows for matching sequences that are similar, but not exact. When primary sequence percent identity falls below about 25%, algorithms often fail to identify proteins that may have similar 3D structure. We have created a hydrophobicity scale and a matching dynamic programming algorithm called TMATCH (unpublished report) that is able to match proteins with remote homologs with similar secondary/tertiary structure, even with very low primary sequence matches. In this paper, we describe how we arrived at the hydrophobic scale, how it provides much more information than percent identity matches and some of the implications for better alignments and understanding protein structure.

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Cavanaugh, David, and Krishnan Chittur. "A hydrophobic proclivity index for protein alignments." F1000Research 4 (October 15, 2020): 1097. http://dx.doi.org/10.12688/f1000research.6348.2.

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Sequence alignment algorithms are fundamental to modern bioinformatics. Sequence alignments are widely used in diverse applications such as phylogenetic analysis, database searches for related sequences to aid identification of unknown protein domain structures and classification of proteins and protein domains. Additionally, alignment algorithms are integral to the location of related proteins to secure understanding of unknown protein functions, to suggest the folded structure of proteins of unknown structure from location of homologous proteins and/or by locating homologous domains of known 3D structure. For proteins, alignment algorithms depend on information about amino acid substitutions that allows for matching sequences that are similar, but not exact. When primary sequence percent identity falls below about 25%, algorithms often fail to identify proteins that may have similar 3D structure. We have created a hydrophobicity scale and a matching dynamic programming algorithm called TMATCH (preprint report) that is able to match proteins with remote homologs with similar secondary/tertiary structure, even with very low primary sequence matches. In this paper, we describe how we arrived at the hydrophobic scale, how it provides much more information than percent identity matches and some of the implications for better alignments and understanding protein structure.

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Begleiter, R., R. El-Yaniv, and G. Yona. "On Prediction Using Variable Order Markov Models." Journal of Artificial Intelligence Research 22 (December 1, 2004): 385–421. http://dx.doi.org/10.1613/jair.1491.

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This paper is concerned with algorithms for prediction of discrete sequences over a finite alphabet, using variable order Markov models. The class of such algorithms is large and in principle includes any lossless compression algorithm. We focus on six prominent prediction algorithms, including Context Tree Weighting (CTW), Prediction by Partial Match (PPM) and Probabilistic Suffix Trees (PSTs). We discuss the properties of these algorithms and compare their performance using real life sequences from three domains: proteins, English text and music pieces. The comparison is made with respect to prediction quality as measured by the average log-loss. We also compare classification algorithms based on these predictors with respect to a number of large protein classification tasks. Our results indicate that a ``decomposed'' CTW (a variant of the CTW algorithm) and PPM outperform all other algorithms in sequence prediction tasks. Somewhat surprisingly, a different algorithm, which is a modification of the Lempel-Ziv compression algorithm, significantly outperforms all algorithms on the protein classification problems.

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Moschopoulos, Charalampos, Grigorios Beligiannis, Spiridon Likothanassis, and Sophia Kossida. "Using a Genetic Algorithm and Markov Clustering on Protein–Protein Interaction Graphs." International Journal of Systems Biology and Biomedical Technologies 1, no. 2 (April 2012): 35–47. http://dx.doi.org/10.4018/ijsbbt.2012040103.

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In this paper, a Genetic Algorithm is applied on the filter of the Enhanced Markov Clustering algorithm to optimize the selection of clusters having a high probability to represent protein complexes. The filter was applied on the results (obtained by experiments made on five different yeast datasets) of three different algorithms known for their efficiency on protein complex detection through protein interaction graphs. The results are compared with three popular clustering algorithms, proving the efficiency of the proposed method according to metrics such as successful prediction rate and geometrical accuracy.

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Wang, Derui, and Jingyu Hou. "Explore the hidden treasure in protein–protein interaction networks — An iterative model for predicting protein functions." Journal of Bioinformatics and Computational Biology 13, no. 05 (October 2015): 1550026. http://dx.doi.org/10.1142/s0219720015500262.

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Protein–protein interaction networks constructed by high throughput technologies provide opportunities for predicting protein functions. A lot of approaches and algorithms have been applied on PPI networks to predict functions of unannotated proteins over recent decades. However, most of existing algorithms and approaches do not consider unannotated proteins and their corresponding interactions in the prediction process. On the other hand, algorithms which make use of unannotated proteins have limited prediction performance. Moreover, current algorithms are usually one-off predictions. In this paper, we propose an iterative approach that utilizes unannotated proteins and their interactions in prediction. We conducted experiments to evaluate the performance and robustness of the proposed iterative approach. The iterative approach maximally improved the prediction performance by 50%–80% when there was a high proportion of unannotated neighborhood protein in the network. The iterative approach also showed robustness in various types of protein interaction network. Importantly, our iterative approach initially proposes an idea that iteratively incorporates the interaction information of unannotated proteins into the protein function prediction and can be applied on existing prediction algorithms to improve prediction performance.

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Dandekar, Thomas, and Patrick Argos. "Potential of genetic algorithms in protein folding and protein engineering simulations." "Protein Engineering, Design and Selection" 5, no. 7 (1992): 637–45. http://dx.doi.org/10.1093/protein/5.7.637.

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Gainza, Pablo, Hunter M. Nisonoff, and Bruce R. Donald. "Algorithms for protein design." Current Opinion in Structural Biology 39 (August 2016): 16–26. http://dx.doi.org/10.1016/j.sbi.2016.03.006.

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Brown, Michael Scott, Tommy Bennett, and James A. Coker. "Niche Genetic Algorithms are better than traditional Genetic Algorithms for de novo Protein Folding." F1000Research 3 (October 7, 2014): 236. http://dx.doi.org/10.12688/f1000research.5412.1.

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Here we demonstrate that Niche Genetic Algorithms (NGA) are better at computing de novo protein folding than traditional Genetic Algorithms (GA). Previous research has shown that proteins can fold into their active forms in a limited number of ways; however, predicting how a set of amino acids will fold starting from the primary structure is still a mystery. GAs have a unique ability to solve these types of scientific problems because of their computational efficiency. Unfortunately, GAs are generally quite poor at solving problems with multiple optima. However, there is a special group of GAs called Niche Genetic Algorithms (NGA) that are quite good at solving problems with multiple optima. In this study, we use a specific NGA: the Dynamic-radius Species-conserving Genetic Algorithm (DSGA), and show that DSGA is very adept at predicting the folded state of proteins, and that DSGA is better than a traditional GA in deriving the correct folding pattern of a protein.

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Khatami, Mohammad Hassan, Udson C. Mendes, Nathan Wiebe, and Philip M. Kim. "Gate-based quantum computing for protein design." PLOS Computational Biology 19, no. 4 (April 12, 2023): e1011033. http://dx.doi.org/10.1371/journal.pcbi.1011033.

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Protein design is a technique to engineer proteins by permuting amino acids in the sequence to obtain novel functionalities. However, exploring all possible combinations of amino acids is generally impossible due to the exponential growth of possibilities with the number of designable sites. The present work introduces circuits implementing a pure quantum approach, Grover’s algorithm, to solve protein design problems. Our algorithms can adjust to implement any custom pair-wise energy tables and protein structure models. Moreover, the algorithm’s oracle is designed to consist of only adder functions. Quantum computer simulators validate the practicality of our circuits, containing up to 234 qubits. However, a smaller circuit is implemented on real quantum devices. Our results show that using O(N) iterations, the circuits find the correct results among all N possibilities, providing the expected quadratic speed up of Grover’s algorithm over classical methods (i.e., O(N)).

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Lappe, M., and L. Holm. "Algorithms for protein interaction networks." Biochemical Society Transactions 33, no. 3 (June 1, 2005): 530–34. http://dx.doi.org/10.1042/bst0330530.

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The functional characterization of all genes and their gene products is the main challenge of the postgenomic era. Recent experimental and computational techniques have enabled the study of interactions among all proteins on a large scale. In this paper, approaches will be presented to exploit interaction information for the inference of protein structure, function, signalling pathways and ultimately entire interactomes. Interaction networks can be modelled as graphs, showing the operation of gene function in terms of protein interactions. Since the architecture of biological networks differs distinctly from random networks, these functional maps contain a signal that can be used for predictive purposes. Protein function and structure can be predicted by matching interaction patterns, without the requirement of sequence similarity. Moving on to a higher level definition of protein function, the question arises how to decompose complex networks into meaningful subsets. An algorithm will be demonstrated, which extracts whole signal-transduction pathways from noisy graphs derived from text-mining the biological literature. Finally, an algorithmic strategy is formulated that enables the proteomics community to build a reliable scaffold of the interactome in a fraction of the time compared with uncoordinated efforts.

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Shirmohammady, Naeem, Habib Izadkhah, and Ayaz Isazadeh. "PPI-GA: A Novel Clustering Algorithm to Identify Protein Complexes within Protein-Protein Interaction Networks Using Genetic Algorithm." Complexity 2021 (March 25, 2021): 1–14. http://dx.doi.org/10.1155/2021/2132516.

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Comprehensive analysis of proteins to evaluate their genetic diversity, study their differences, and respond to the tensions is the main subject of an interdisciplinary field of study called proteomics. The main objective of the proteomics is to detect and quantify proteins and study their post-translational modifications and interactions using protein chemistry, bioinformatics, and biology. Any disturbance in proteins interactive network can act as a source for biological disorders and various diseases such as Alzheimer and cancer. Most current computational methods for discovering protein complexes are usually based on specific topological characteristics of protein-protein networks (PPI). To identify the protein complexes, in this paper, we, first, present a new encoding method to represent solutions; we then propose a new clustering algorithm based on the genetic algorithm, named PPI-GA, employing a new multiobjective quality function. The proposed algorithm is evaluated on two gold standard and real-world datasets. The result achieved demonstrates that the proposed algorithm can detect important protein complexes, and it provides more accurate results compared with state-of-the-art protein complex identification algorithms.

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DERONNE, KEVIN W., and GEORGE KARYPIS. "EFFECTIVE OPTIMIZATION ALGORITHMS FOR FRAGMENT-ASSEMBLY BASED PROTEIN STRUCTURE PREDICTION." Journal of Bioinformatics and Computational Biology 05, no. 02a (April 2007): 335–52. http://dx.doi.org/10.1142/s0219720007002618.

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Despite recent developments in protein structure prediction, an accurate new fold prediction algorithm remains elusive. One of the challenges facing current techniques is the size and complexity of the space containing possible structures for a query sequence. Traditionally, to explore this space fragment assembly approaches to new fold prediction have used stochastic optimization techniques. Here, we examine deterministic algorithms for optimizing scoring functions in protein structure prediction. Two previously unused techniques are applied to the problem, called the Greedy algorithm and the Hill-climbing (HC) algorithm. The main difference between the two is that the latter implements a technique to overcome local minima. Experiments on a diverse set of 276 proteins show that the HC algorithms consistently outperform existing approaches based on Simulated Annealing optimization (a traditional stochastic technique) in optimizing the root mean squared deviation between native and working structures.

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CHUA, HON NIAN, KANG NING, WING-KIN SUNG, HON WAI LEONG, and LIMSOON WONG. "USING INDIRECT PROTEIN–PROTEIN INTERACTIONS FOR PROTEIN COMPLEX PREDICTION." Journal of Bioinformatics and Computational Biology 06, no. 03 (June 2008): 435–66. http://dx.doi.org/10.1142/s0219720008003497.

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Protein complexes are fundamental for understanding principles of cellular organizations. As the sizes of protein–protein interaction (PPI) networks are increasing, accurate and fast protein complex prediction from these PPI networks can serve as a guide for biological experiments to discover novel protein complexes. However, it is not easy to predict protein complexes from PPI networks, especially in situations where the PPI network is noisy and still incomplete. Here, we study the use of indirect interactions between level-2 neighbors (level-2 interactions) for protein complex prediction. We know from previous work that proteins which do not interact but share interaction partners (level-2 neighbors) often share biological functions. We have proposed a method in which all direct and indirect interactions are first weighted using topological weight (FS-Weight), which estimates the strength of functional association. Interactions with low weight are removed from the network, while level-2 interactions with high weight are introduced into the interaction network. Existing clustering algorithms can then be applied to this modified network. We have also proposed a novel algorithm that searches for cliques in the modified network, and merge cliques to form clusters using a "partial clique merging" method. Experiments show that (1) the use of indirect interactions and topological weight to augment protein–protein interactions can be used to improve the precision of clusters predicted by various existing clustering algorithms; and (2) our complex-finding algorithm performs very well on interaction networks modified in this way. Since no other information except the original PPI network is used, our approach would be very useful for protein complex prediction, especially for prediction of novel protein complexes.

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Ruiz Echartea, Maria Elisa, Isaure Chauvot de Beauchêne, and David W. Ritchie. "EROS-DOCK: protein–protein docking using exhaustive branch-and-bound rotational search." Bioinformatics 35, no. 23 (May 24, 2019): 5003–10. http://dx.doi.org/10.1093/bioinformatics/btz434.

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Abstract Motivation Protein–protein docking algorithms aim to predict the 3D structure of a binary complex using the structures of the individual proteins. This typically involves searching and scoring in a 6D space. Many docking algorithms use FFT techniques to exhaustively cover the search space and to accelerate the scoring calculation. However, FFT docking results often depend on the initial protein orientations with respect to the Fourier sampling grid. Furthermore, Fourier-transforming a physics-base force field can involve a serious loss of precision. Results Here, we present EROS-DOCK, an algorithm to rigidly dock two proteins using a series of exhaustive 3D rotational searches in which non-clashing orientations are scored using the ATTRACT coarse-grained force field model. The rotational space is represented as a quaternion ‘π-ball’, which is systematically sub-divided in a ‘branch-and-bound’ manner, allowing efficient pruning of rotations that will give steric clashes. The algorithm was tested on 173 Docking Benchmark complexes, and results were compared with those of ATTRACT and ZDOCK. According to the CAPRI quality criteria, EROS-DOCK typically gives more acceptable or medium quality solutions than ATTRACT and ZDOCK. Availability and implementation The EROS-DOCK program is available for download at http://erosdock.loria.fr. Supplementary information Supplementary data are available at Bioinformatics online.

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Dunham, Brandan, and Madhavi K. Ganapathiraju. "Benchmark Evaluation of Protein–Protein Interaction Prediction Algorithms." Molecules 27, no. 1 (December 22, 2021): 41. http://dx.doi.org/10.3390/molecules27010041.

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Protein–protein interactions (PPIs) perform various functions and regulate processes throughout cells. Knowledge of the full network of PPIs is vital to biomedical research, but most of the PPIs are still unknown. As it is infeasible to discover all of them experimentally due to technical and resource limitations, computational prediction of PPIs is essential and accurately assessing the performance of algorithms is required before further application or translation. However, many published methods compose their evaluation datasets incorrectly, using a higher proportion of positive class data than occuring naturally, leading to exaggerated performance. We re-implemented various published algorithms and evaluated them on datasets with realistic data compositions and found that their performance is overstated in original publications; with several methods outperformed by our control models built on ‘illogical’ and random number features. We conclude that these methods are influenced by an over-characterization of some proteins in the literature and due to scale-free nature of PPI network and that they fail when tested on all possible protein pairs. Additionally, we found that sequence-only-based algorithms performed worse than those that employ functional and expression features. We present a benchmark evaluation of many published algorithms for PPI prediction. The source code of our implementations and the benchmark datasets created here are made available in open source.

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Vreven, Thom, Howook Hwang, and Zhiping Weng. "Exploring Angular Distance in Protein-Protein Docking Algorithms." PLoS ONE 8, no. 2 (February 21, 2013): e56645. http://dx.doi.org/10.1371/journal.pone.0056645.

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Hallen, Mark A., and Bruce R. Donald. "Protein design by provable algorithms." Communications of the ACM 62, no. 10 (September 24, 2019): 76–84. http://dx.doi.org/10.1145/3338124.

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Arriagada, Mauricio, and Aleksandar Poleksic. "On the Difference in Quality between Current Heuristic and Optimal Solutions to the Protein Structure Alignment Problem." BioMed Research International 2013 (2013): 1–8. http://dx.doi.org/10.1155/2013/459248.

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The importance of pairwise protein structural comparison in biomedical research is fueling the search for algorithms capable of finding more accurate structural match of two input proteins in a timely manner. In recent years, we have witnessed rapid advances in the development of methods for approximate and optimal solutions to the protein structure matching problem. Albeit slow, these methods can be extremely useful in assessing the accuracy of more efficient, heuristic algorithms. We utilize a recently developed approximation algorithm for protein structure matching to demonstrate that a deep search of the protein superposition space leads to increased alignment accuracy with respect to many well-established measures of alignment quality. The results of our study suggest that a large and important part of the protein superposition space remains unexplored by current techniques for protein structure alignment.

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Sánchez-Hernández, Juan P., Juan Frausto-Solís, Juan J. González-Barbosa, Diego A. Soto-Monterrubio, Fanny G. Maldonado-Nava, and Guadalupe Castilla-Valdez. "A Peptides Prediction Methodology for Tertiary Structure Based on Simulated Annealing." Mathematical and Computational Applications 26, no. 2 (April 29, 2021): 39. http://dx.doi.org/10.3390/mca26020039.

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The Protein Folding Problem (PFP) is a big challenge that has remained unsolved for more than fifty years. This problem consists of obtaining the tertiary structure or Native Structure (NS) of a protein knowing its amino acid sequence. The computational methodologies applied to this problem are classified into two groups, known as Template-Based Modeling (TBM) and ab initio models. In the latter methodology, only information from the primary structure of the target protein is used. In the literature, Hybrid Simulated Annealing (HSA) algorithms are among the best ab initio algorithms for PFP; Golden Ratio Simulated Annealing (GRSA) is a PFP family of these algorithms designed for peptides. Moreover, for the algorithms designed with TBM, they use information from a target protein’s primary structure and information from similar or analog proteins. This paper presents GRSA-SSP methodology that implements a secondary structure prediction to build an initial model and refine it with HSA algorithms. Additionally, we compare the performance of the GRSAX-SSP algorithms versus its corresponding GRSAX. Finally, our best algorithm GRSAX-SSP is compared with PEP-FOLD3, I-TASSER, QUARK, and Rosetta, showing that it competes in small peptides except when predicting the largest peptides.

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SOHAEE, NASSIM, and CHRISTIAN V. FORST. "IDENTIFICATION OF FUNCTIONAL MODULES IN A PPI NETWORK BY BOUNDED DIAMETER CLUSTERING." Journal of Bioinformatics and Computational Biology 08, no. 06 (December 2010): 929–43. http://dx.doi.org/10.1142/s0219720010005221.

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Dense subgraphs of Protein–Protein Interaction (PPI) graphs are assumed to be potential functional modules and play an important role in inferring the functional behavior of proteins. Increasing amount of available PPI data implies a fast, accurate approach of biological complex identification. Therefore, there are different models and algorithms in identifying functional modules. This paper describes a new graph theoretic clustering algorithm that detects densely connected regions in a large PPI graph. The method is based on finding bounded diameter subgraphs around a seed node. The algorithm has the advantage of being very simple and efficient when compared with other graph clustering methods. This algorithm is tested on the yeast PPI graph and the results are compared with MCL, Core-Attachment, and MCODE algorithms.

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Wang, Fengjuan, Cheng Xu, Shufeng Jiang, and Fengxia Xu. "Application of improved intelligent ant colony algorithm in protein folding prediction." Journal of Algorithms & Computational Technology 14 (January 2020): 174830262094141. http://dx.doi.org/10.1177/1748302620941411.

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While the single ant colony algorithm and the fish swarm algorithm have many advantages, they also have various shortcomings. After analyzing the advantages and disadvantages of the ant colony algorithm and the fish swarm algorithm, this paper uses the complementary principle of the two algorithms to effectively fuse the two population intelligent algorithms. The improved swarm intelligence algorithm is applied to the well-considered protein folding prediction problem, and the simplified protein structure Toy model is verified, and the ideal results are obtained. The improved algorithm enhances the search ability, and the computational efficiency is greatly improved, ensuring the accuracy of the operation.

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Kumar, Ashish, Roheet Bhatnagar, Sumit Srivastava, and Arjun Chauhan. "Comparative Prediction of Wine Quality and Protein Synthesis Using ARSkNN." International Journal of Information Technology Project Management 11, no. 4 (October 2020): 31–41. http://dx.doi.org/10.4018/ijitpm.2020100103.

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The amount of data available and information over the past few decades has grown manifold and will only increase exponentially. The ability to harvest and manipulate information from this data has become a crucial activity for effective and faster development. Multiple algorithms and approaches have been developed in order to harvest information from this data. These algorithms have different approaches and therefore result in varied outputs in terms of performance and interpretation. Due to their functionality, different algorithms perform differently on different datasets. In order to compare the effectiveness of these algorithms, they are run on different datasets under a given set of fixed restrictions (e.g., hardware platform, etc.). This paper is an in-depth analysis of different algorithms based on trivial classifier algorithm, kNN, and the newly developed ARSkNN. The algorithms were executed on three different datasets, and analysis was done by evaluating their performance taking into consideration the accuracy percentage and execution time as performance measures.

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Wu, Hongjie, Haiou Li, Min Jiang, Cheng Chen, Qiang Lv, and Chuang Wu. "Identify High-Quality Protein Structural Models by EnhancedK-Means." BioMed Research International 2017 (2017): 1–9. http://dx.doi.org/10.1155/2017/7294519.

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Background.One critical issue in protein three-dimensional structure prediction using either ab initio or comparative modeling involves identification of high-quality protein structural models from generated decoys. Currently, clustering algorithms are widely used to identify near-native models; however, their performance is dependent upon different conformational decoys, and, for some algorithms, the accuracy declines when the decoy population increases.Results.Here, we proposed two enhancedK-means clustering algorithms capable of robustly identifying high-quality protein structural models. The first one employs the clustering algorithm SPICKER to determine the initial centroids for basicK-means clustering (SK-means), whereas the other employs squared distance to optimize the initial centroids (K-means++). Our results showed thatSK-means andK-means++ were more robust as compared with SPICKER alone, detecting 33 (59%) and 42 (75%) of 56 targets, respectively, with template modeling scores better than or equal to those of SPICKER.Conclusions.We observed that the classicK-means algorithm showed a similar performance to that of SPICKER, which is a widely used algorithm for protein-structure identification. BothSK-means andK-means++ demonstrated substantial improvements relative to results from SPICKER and classicalK-means.

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YE, JIEPING, RAVI JANARDAN, and SONGTAO LIU. "PAIRWISE PROTEIN STRUCTURE ALIGNMENT BASED ON AN ORIENTATION-INDEPENDENT BACKBONE REPRESENTATION." Journal of Bioinformatics and Computational Biology 02, no. 04 (December 2004): 699–717. http://dx.doi.org/10.1142/s021972000400082x.

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Determining structural similarities between proteins is an important problem since it can help identify functional and evolutionary relationships. In this paper, an algorithm is proposed to align two protein structures. Given the protein backbones, the algorithm finds a rigid motion of one backbone onto the other such that large substructures are matched. The algorithm uses a representation of the backbones that is independent of their relative orientations in space and applies dynamic programming to this representation to compute an initial alignment, which is then refined iteratively. Experiments indicate that the algorithm is competitive with two well-known algorithms, namely DALI and LOCK.

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Kotelnikova, Ekaterina, Klaus M. Frahm, Dima L. Shepelyansky, and Oksana Kunduzova. "Fibrosis Protein-Protein Interactions from Google Matrix Analysis of MetaCore Network." International Journal of Molecular Sciences 23, no. 1 (December 22, 2021): 67. http://dx.doi.org/10.3390/ijms23010067.

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Protein–protein interactions is a longstanding challenge in cardiac remodeling processes and heart failure. Here, we use the MetaCore network and the Google matrix algorithms for prediction of protein–protein interactions dictating cardiac fibrosis, a primary cause of end-stage heart failure. The developed algorithms allow identification of interactions between key proteins and predict new actors orchestrating fibroblast activation linked to fibrosis in mouse and human tissues. These data hold great promise for uncovering new therapeutic targets to limit myocardial fibrosis.

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Westhead, D. R., V. P. Collura, M. D. Eldridge, M. A. Firth, J. Li, and C. W. Murray. "Protein fold recognition by threading: comparison of algorithms and analysis of results." "Protein Engineering, Design and Selection" 8, no. 12 (1995): 1197–204. http://dx.doi.org/10.1093/protein/8.12.1197.

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DUKKA BAHADUR, K. C., ETSUJI TOMITA, JUN'ICHI SUZUKI, and TATSUYA AKUTSU. "PROTEIN SIDE-CHAIN PACKING PROBLEM: A MAXIMUM EDGE-WEIGHT CLIQUE ALGORITHMIC APPROACH." Journal of Bioinformatics and Computational Biology 03, no. 01 (February 2005): 103–26. http://dx.doi.org/10.1142/s0219720005000904.

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"Protein Side-chain Packing" has an ever-increasing application in the field of bio-informatics, dating from the early methods of homology modeling to protein design and to the protein docking. However, this problem is computationally known to be NP-hard. In this regard, we have developed a novel approach to solve this problem using the notion of a maximum edge-weight clique. Our approach is based on efficient reduction of protein side-chain packing problem to a graph and then solving the reduced graph to find the maximum clique by applying an efficient clique finding algorithm developed by our co-authors. Since our approach is based on deterministic algorithms in contrast to the various existing algorithms based on heuristic approaches, our algorithm guarantees of finding an optimal solution. We have tested this approach to predict the side-chain conformations of a set of proteins and have compared the results with other existing methods. We have found that our results are favorably comparable or better than the results produced by the existing methods. As our test set contains a protein of 494 residues, we have obtained considerable improvement in terms of size of the proteins and in terms of the efficiency and the accuracy of prediction.

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Lin, Guohui, Dong Xu, Zhi-Zhong Chen, Tao Jiang, Jianjun Wen, and Ying Xu. "Computational Assignment of Protein Backbone NMR Peaks by Efficient Bounding and Filtering." Journal of Bioinformatics and Computational Biology 01, no. 02 (July 2003): 387–409. http://dx.doi.org/10.1142/s0219720003000083.

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NMR resonance assignment is one of the key steps in solving an NMR protein structure. The assignment process links resonance peaks to individual residues of the target protein sequence, providing the prerequisite for establishing intra- and inter-residue spatial relationships between atoms. The assignment process is tedious and time-consuming, which could take many weeks. Though there exist a number of computer programs to assist the assignment process, many NMR labs are still doing the assignments manually to ensure quality. This paper presents a new computational method based on the combination of a suite of algorithms for automating the assignment process, particularly the process of backbone resonance peak assignment. We formulate the assignment problem as a constrained weighted bipartite matching problem. While the problem, in the most general situation, is NP-hard, we present an efficient solution based on a branch-and-bound algorithm with effective bounding techniques using two recently introduced approximation algorithms. We also devise a greedy filtering algorithm for reducing the search space. Our experimental results on 70 instances of (pseudo) real NMR data derived from 14 proteins demonstrate that the new solution runs much faster than a recently introduced (exhaustive) two-layer algorithm and recovers more correct peak assignments than the two-layer algorithm. Our result demonstrates that integrating different algorithms can achieve a good tradeoff between backbone assignment accuracy and computation time.

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Sunny, Sharon, and P. B. Jayaraj. "A Geometric Complementarity-Based Tool for Protein–Protein Docking." Journal of Computational Biophysics and Chemistry 21, no. 01 (December 9, 2021): 35–46. http://dx.doi.org/10.1142/s273741652250003x.

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The computationally hard protein–protein complex structure prediction problem is continuously fascinating to the scientific community due to its biological impact. The field has witnessed the application of geometric algorithms, randomized algorithms, and evolutionary algorithms to name a few. These techniques improve either the searching or scoring phase. An effective searching strategy does not generate a large conformation space that perhaps demands computational power. Another determining factor is the parameter chosen for score calculation. The proposed method is an attempt to curtail the conformations by limiting the search procedure to probable regions. In this method, partial derivatives are calculated on the coarse-grained representation of the surface residues to identify the optimal points on the protein surface. Contrary to the existing geometric-based algorithms that align the convex and concave regions of both proteins, this method aligns the concave regions of the receptor with convex regions of the ligand only and thus reduces the size of conformation space. The method’s performance is evaluated using the 55 newly added targets in Protein–Protein Docking Benchmark v 5 and is found to be successful for around 47% of the targets.

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Lo, Victor L., Richard L. Kingston, and Rick P. Millane. "Iterative projection algorithms in protein crystallography. II. Application." Acta Crystallographica Section A Foundations and Advances 71, no. 4 (June 6, 2015): 451–59. http://dx.doi.org/10.1107/s2053273315005574.

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Iterative projection algorithms (IPAs) are a promising tool for protein crystallographic phase determination. Although related to traditional density-modification algorithms, IPAs have better convergence properties, and, as a result, can effectively overcome the phase problem given modest levels of structural redundancy. This is illustrated by applying IPAs to determine the electron densities of two protein crystals with fourfold non-crystallographic symmetry, starting with only the experimental diffraction amplitudes, a low-resolution molecular envelope and the position of the non-crystallographic axes. The algorithm returns electron densities that are sufficiently accurate for model building, allowing automated recovery of the known structures. This study indicates that IPAs should find routine application in protein crystallography, being capable of reconstructing electron densities starting with very little initial phase information.

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Patrick, W. M., A. E. Firth, and J. M. Blackburn. "User-friendly algorithms for estimating completeness and diversity in randomized protein-encoding libraries." Protein Engineering Design and Selection 16, no. 6 (June 1, 2003): 451–57. http://dx.doi.org/10.1093/protein/gzg057.

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Shen, Li, Jian Zhang, Fang Wang, and Kai Liu. "Predicting Essential Proteins Based on Integration of Local Fuzzy Fractal Dimension and Subcellular Location Information." Genes 13, no. 2 (January 19, 2022): 173. http://dx.doi.org/10.3390/genes13020173.

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Essential proteins are indispensable to cells’ survival and development. Prediction and analysis of essential proteins are crucial for uncovering the mechanisms of cells. With the help of computer science and high-throughput technologies, forecasting essential proteins by protein–protein interaction (PPI) networks has become more efficient than traditional approaches (expensive experimental methods are generally used). Many computational algorithms were employed to predict the essential proteins; however, they have various restrictions. To improve the prediction accuracy, by introducing the Local Fuzzy Fractal Dimension (LFFD) of complex networks into the analysis of the PPI network, we propose a novel algorithm named LDS, which combines the LFFD of the PPI network with the protein subcellular location information. By testing the proposed LDS algorithm on three different yeast PPI networks, the experimental results show that LDS outperforms some state-of-the-art essential protein-prediction techniques.

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Huang, Chien-Hung, Huai-Shun Peng, and Ka-Lok Ng. "Prediction of Cancer Proteins by Integrating Protein Interaction, Domain Frequency, and Domain Interaction Data Using Machine Learning Algorithms." BioMed Research International 2015 (2015): 1–15. http://dx.doi.org/10.1155/2015/312047.

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Many proteins are known to be associated with cancer diseases. It is quite often that their precise functional role in disease pathogenesis remains unclear. A strategy to gain a better understanding of the function of these proteins is to make use of a combination of different aspects of proteomics data types. In this study, we extended Aragues’s method by employing the protein-protein interaction (PPI) data, domain-domain interaction (DDI) data, weighted domain frequency score (DFS), and cancer linker degree (CLD) data to predict cancer proteins. Performances were benchmarked based on three kinds of experiments as follows: (I) using individual algorithm, (II) combining algorithms, and (III) combining the same classification types of algorithms. When compared with Aragues’s method, our proposed methods, that is, machine learning algorithm and voting with the majority, are significantly superior in all seven performance measures. We demonstrated the accuracy of the proposed method on two independent datasets. The best algorithm can achieve a hit ratio of 89.4% and 72.8% for lung cancer dataset and lung cancer microarray study, respectively. It is anticipated that the current research could help understand disease mechanisms and diagnosis.

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Helles, Glennie. "A comparative study of the reported performance of ab initio protein structure prediction algorithms." Journal of The Royal Society Interface 5, no. 21 (December 11, 2007): 387–96. http://dx.doi.org/10.1098/rsif.2007.1278.

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Protein structure prediction is one of the major challenges in bioinformatics today. Throughout the past five decades, many different algorithmic approaches have been attempted, and although progress has been made the problem remains unsolvable even for many small proteins. While the general objective is to predict the three-dimensional structure from primary sequence, our current knowledge and computational power are simply insufficient to solve a problem of such high complexity. Some prediction algorithms do, however, appear to perform better than others, although it is not always obvious which ones they are and it is perhaps even less obvious why that is. In this review, the reported performance results from 18 different recently published prediction algorithms are compared. Furthermore, the general algorithmic settings most likely responsible for the difference in the reported performance are identified, and the specific settings of each of the 18 prediction algorithms are also compared. The average normalized r.m.s.d. scores reported range from 11.17 to 3.48. With a performance measure including both r.m.s.d. scores and CPU time, the currently best-performing prediction algorithm is identified to be the I-TASSER algorithm. Two of the algorithmic settings—protein representation and fragment assembly—were found to have definite positive influence on the running time and the predicted structures, respectively. There thus appears to be a clear benefit from incorporating this knowledge in the design of new prediction algorithms.

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Hung, Che-Lun, and Yaw-Ling Lin. "Implementation of a Parallel Protein Structure Alignment Service on Cloud." International Journal of Genomics 2013 (2013): 1–8. http://dx.doi.org/10.1155/2013/439681.

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Protein structure alignment has become an important strategy by which to identify evolutionary relationships between protein sequences. Several alignment tools are currently available for online comparison of protein structures. In this paper, we propose a parallel protein structure alignment service based on the Hadoop distribution framework. This service includes a protein structure alignment algorithm, a refinement algorithm, and a MapReduce programming model. The refinement algorithm refines the result of alignment. To process vast numbers of protein structures in parallel, the alignment and refinement algorithms are implemented using MapReduce. We analyzed and compared the structure alignments produced by different methods using a dataset randomly selected from the PDB database. The experimental results verify that the proposed algorithm refines the resulting alignments more accurately than existing algorithms. Meanwhile, the computational performance of the proposed service is proportional to the number of processors used in our cloud platform.

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BERGER, BONNIE. "Algorithms for Protein Structural Motif Recognition." Journal of Computational Biology 2, no. 1 (January 1995): 125–38. http://dx.doi.org/10.1089/cmb.1995.2.125.

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Cherfils, Jacqueline, and Joël Janin. "Protein docking algorithms: simulating molecular recognition." Current Opinion in Structural Biology 3, no. 2 (April 1993): 265–69. http://dx.doi.org/10.1016/s0959-440x(05)80162-9.

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Pedersen, Jan T., and John Moult. "Genetic algorithms for protein structure prediction." Current Opinion in Structural Biology 6, no. 2 (April 1996): 227–31. http://dx.doi.org/10.1016/s0959-440x(96)80079-0.

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Poleksic, Aleksandar. "Algorithms for optimal protein structure alignment." Bioinformatics 25, no. 21 (September 4, 2009): 2751–56. http://dx.doi.org/10.1093/bioinformatics/btp530.

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Unger, Ron, and John Moult. "Genetic Algorithms for Protein Folding Simulations." Journal of Molecular Biology 231, no. 1 (May 1993): 75–81. http://dx.doi.org/10.1006/jmbi.1993.1258.

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Oakley, Aaron J. "Hidden Glutathione Transferases in the Human Genome." Biomolecules 13, no. 8 (August 12, 2023): 1240. http://dx.doi.org/10.3390/biom13081240.

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With the development of accurate protein structure prediction algorithms, artificial intelligence (AI) has emerged as a powerful tool in the field of structural biology. AI-based algorithms have been used to analyze large amounts of protein sequence data including the human proteome, complementing experimental structure data found in resources such as the Protein Data Bank. The EBI AlphaFold Protein Structure Database (for example) contains over 230 million structures. In this study, these data have been analyzed to find all human proteins containing (or predicted to contain) the cytosolic glutathione transferase (cGST) fold. A total of 39 proteins were found, including the alpha-, mu-, pi-, sigma-, zeta- and omega-class GSTs, intracellular chloride channels, metaxins, multisynthetase complex components, elongation factor 1 complex components and others. Three broad themes emerge: cGST domains as enzymes, as chloride ion channels and as protein–protein interaction mediators. As the majority of cGSTs are dimers, the AI-based structure prediction algorithm AlphaFold-multimer was used to predict structures of all pairwise combinations of these cGST domains. Potential homo- and heterodimers are described. Experimental biochemical and structure data is used to highlight the strengths and limitations of AI-predicted structures.

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Caliandro, Rocco, Benedetta Carrozzini, Giovanni Luca Cascarano, Giuliana Comunale, Carmelo Giacovazzo, and Annamaria Mazzone. "Protein phasing at non-atomic resolution by combining Patterson andVLDtechniques." Acta Crystallographica Section D Biological Crystallography 70, no. 7 (June 29, 2014): 1994–2006. http://dx.doi.org/10.1107/s139900471401013x.

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Phasing proteins at non-atomic resolution is still a challenge for anyab initiomethod. A variety of algorithms [Patterson deconvolution, superposition techniques, a cross-correlation function (Cmap), theVLD(vive la difference) approach, the FF function, a nonlinear iterative peak-clipping algorithm (SNIP) for defining the background of a map and thefree lunchextrapolation method] have been combined to overcome the lack of experimental information at non-atomic resolution. The method has been applied to a large number of protein diffraction data sets with resolutions varying from atomic to 2.1 Å, with the condition that S or heavier atoms are present in the protein structure. The applications include the use ofARP/wARPto check the quality of the final electron-density maps in an objective way. The results show that resolution is still the maximum obstacle to protein phasing, but also suggest that the solution of protein structures at 2.1 Å resolution is a feasible, even if still an exceptional, task for the combined set of algorithms implemented in the phasing program. The approach described here is more efficient than the previously described procedures:e.g.the combined use of the algorithms mentioned above is frequently able to provide phases of sufficiently high quality to allow automatic model building. The method is implemented in the current version ofSIR2014.

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Kumar, Nilesh, and M. Shahid Mukhtar. "Ranking Plant Network Nodes Based on Their Centrality Measures." Entropy 25, no. 4 (April 18, 2023): 676. http://dx.doi.org/10.3390/e25040676.

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Biological networks are often large and complex, making it difficult to accurately identify the most important nodes. Node prioritization algorithms are used to identify the most influential nodes in a biological network by considering their relationships with other nodes. These algorithms can help us understand the functioning of the network and the role of individual nodes. We developed CentralityCosDist, an algorithm that ranks nodes based on a combination of centrality measures and seed nodes. We applied this and four other algorithms to protein–protein interactions and co-expression patterns in Arabidopsis thaliana using pathogen effector targets as seed nodes. The accuracy of the algorithms was evaluated through functional enrichment analysis of the top 10 nodes identified by each algorithm. Most enriched terms were similar across algorithms, except for DIAMOnD. CentralityCosDist identified more plant–pathogen interactions and related functions and pathways compared to the other algorithms.

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Liang, Zhengping, Rui Guo, Jiangtao Sun, Zhong Ming, and Zexuan Zhu. "Orderly Roulette Selection Based Ant Colony Algorithm for Hierarchical Multilabel Protein Function Prediction." Mathematical Problems in Engineering 2017 (2017): 1–15. http://dx.doi.org/10.1155/2017/6320273.

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Ant colony optimization (ACO) algorithms have been successfully applied to identify classification rules in data mining. This paper proposes a new ant colony optimization algorithm, named hmAntMinerorder, for the hierarchical multilabel classification problem in protein function prediction. The proposed algorithm is characterized by an orderly roulette selection strategy that distinguishes the merits of the data attributes through attributes importance ranking in classification model construction. A new pheromone update strategy is introduced to prevent the algorithm from getting trapped in local optima and thus leading to more efficient identification of classification rules. The comparison studies to other closely related algorithms on 16 publicly available datasets reveal the efficiency of the proposed algorithm.

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Zahiri, Javad, Joseph Bozorgmehr, and Ali Masoudi-Nejad. "Computational Prediction of Protein–Protein Interaction Networks: Algorithms and Resources." Current Genomics 14, no. 6 (September 1, 2013): 397–414. http://dx.doi.org/10.2174/1389202911314060004.

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CHANG, DARBY TIEN-HAO, JUNG-HSIN LIN, CHIH-HUNG HSIEH, and YEN-JENG OYANG. "ON THE DESIGN OF OPTIMIZATION ALGORITHMS FOR PREDICTION OF MOLECULAR INTERACTIONS." International Journal on Artificial Intelligence Tools 19, no. 03 (June 2010): 267–80. http://dx.doi.org/10.1142/s0218213010000182.

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This article presents a comprehensive study on the main characteristics of a novel optimization algorithm specifically designed for simulation of protein-ligand interactions. Though design of optimization algorithms has been a research issue extensively studied by computer scientists for decades, the emerging applications in bioinformatics such as simulation of protein-ligand interactions and protein folding introduce additional challenges due to (1) the high dimensionality nature of the problem and (2) the highly rugged landscape of the energy function. As a result, optimization algorithms that are not carefully designed to tackle these two challenges may fail to deliver satisfactory performance. This study has been motivated by the observation that the RAME (Rank-based Adaptive Mutation Evolutionary) optimization algorithm specifically designed for simulation of protein-ligand docking has consistently outperformed the conventional optimization algorithms by a significant degree. The experimental results reveal that the RAME algorithm is capable of delivering superior performance to several alternative versions of the genetic algorithm in handling highly-rugged functions in the high-dimensional vector space. This article also reports experiments conducted to analyze the causes of the observed performance difference. The experiences learned provide valuable clues for how the proposed algorithm can be effectively exploited to tackle other computational biology problems.

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Al-hussaniy, Hany Akeel. "The development of molecular docking and molecular dynamics and their application in the field of chemistry and computer simulation." Journal of medical pharmaceutical and allied sciences 12, no. 1 (January 31, 2023): 5552–62. http://dx.doi.org/10.55522/jmpas.v12i1.4137.

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With the rapid development of modern life science, computational Molecular docking has gradually become one of the core disciplines and methods of modern life science research. Computational docking studies the relationship between the structure and pharmacodynamics of biological macromolecules and the interaction between biological macromolecules and ligands. It promotes the development of protein engineering, protein design, and computer-aided drug design with powerful and various docking software in predicting the three-dimensional structure and dynamic characteristics of proteins from protein sequences. Nowadays, this computing power can be provided by the GPU through the use of a general-purpose computing model on GPUs. This article presents two approaches to parallelizing the descriptive algorithms on the GPU to solve the molecular docking problem and then evaluating them in terms of the computation time achieved. The proposed approaches are effective in accelerating molecular docking on GPUs compared to a single-core or multicore CPU. Besides introducing parallelization approaches, we propose a new descriptive algorithm based on the bee swarm algorithm to solve the molecular docking problem as an alternative to traditional descriptive algorithms such as the genetic algorithm.

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Serackis, Artūras, Dalius Matuzevičius, Dalius Navakauskas, Eldar Šabanovič, Andrius Katkevičius, and Darius Plonis. "A Robust Identification of the Protein Standard Bands in Two-Dimensional Electrophoresis Gel Images." Electrical, Control and Communication Engineering 13, no. 1 (December 1, 2017): 63–68. http://dx.doi.org/10.1515/ecce-2017-0009.

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Abstract The aim of the investigation presented in this paper was to develop a software-based assistant for the protein analysis workflow. The prior characterization of the unknown protein in two-dimensional electrophoresis gel images is performed according to the molecular weight and isoelectric point of each protein spot estimated from the gel image before further sequence analysis by mass spectrometry. The paper presents a method for automatic and robust identification of the protein standard band in a two-dimensional gel image. In addition, the method introduces the identification of the positions of the markers, prepared by using pre-selected proteins with known molecular mass. The robustness of the method was achieved by using special validation rules in the proposed original algorithms. In addition, a self-organizing map-based decision support algorithm is proposed, which takes Gabor coefficients as image features and searches for the differences in preselected vertical image bars. The experimental investigation proved the good performance of the new algorithms included into the proposed method. The detection of the protein standard markers works without modification of algorithm parameters on two-dimensional gel images obtained by using different staining and destaining procedures, which results in different average levels of intensity in the images.

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Min, Seonwoo, HyunGi Kim, Byunghan Lee, and Sungroh Yoon. "Protein transfer learning improves identification of heat shock protein families." PLOS ONE 16, no. 5 (May 18, 2021): e0251865. http://dx.doi.org/10.1371/journal.pone.0251865.

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Heat shock proteins (HSPs) play a pivotal role as molecular chaperones against unfavorable conditions. Although HSPs are of great importance, their computational identification remains a significant challenge. Previous studies have two major limitations. First, they relied heavily on amino acid composition features, which inevitably limited their prediction performance. Second, their prediction performance was overestimated because of the independent two-stage evaluations and train-test data redundancy. To overcome these limitations, we introduce two novel deep learning algorithms: (1) time-efficient DeepHSP and (2) high-performance DeeperHSP. We propose a convolutional neural network (CNN)-based DeepHSP that classifies both non-HSPs and six HSP families simultaneously. It outperforms state-of-the-art algorithms, despite taking 14–15 times less time for both training and inference. We further improve the performance of DeepHSP by taking advantage of protein transfer learning. While DeepHSP is trained on raw protein sequences, DeeperHSP is trained on top of pre-trained protein representations. Therefore, DeeperHSP remarkably outperforms state-of-the-art algorithms increasing F1 scores in both cross-validation and independent test experiments by 20% and 10%, respectively. We envision that the proposed algorithms can provide a proteome-wide prediction of HSPs and help in various downstream analyses for pathology and clinical research.

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Journal articles on the topic 'Algorithms- Protein'

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