5,018 research outputs found
Flexible protein folding by ant colony optimization
Protein structure prediction is one of the most challenging topics in bioinformatics.
As the protein structure is found to be closely related to its functions,
predicting the folding structure of a protein to judge its functions is meaningful to
the humanity. This chapter proposes a flexible ant colony (FAC) algorithm for solving
protein folding problems (PFPs) based on the hydrophobic-polar (HP) square lattice
model. Different from the previous ant algorithms for PFPs, the pheromones in the
proposed algorithm are placed on the arcs connecting adjacent squares in the lattice.
Such pheromone placement model is similar to the one used in the traveling salesmen
problems (TSPs), where pheromones are released on the arcs connecting the cities.
Moreover, the collaboration of effective heuristic and pheromone strategies greatly
enhances the performance of the algorithm so that the algorithm can achieve good
results without local search methods. By testing some benchmark two-dimensional
hydrophobic-polar (2D-HP) protein sequences, the performance shows that the proposed
algorithm is quite competitive compared with some other well-known methods
for solving the same protein folding problems
On the entropy of protein families
Proteins are essential components of living systems, capable of performing a
huge variety of tasks at the molecular level, such as recognition, signalling,
copy, transport, ... The protein sequences realizing a given function may
largely vary across organisms, giving rise to a protein family. Here, we
estimate the entropy of those families based on different approaches, including
Hidden Markov Models used for protein databases and inferred statistical models
reproducing the low-order (1-and 2-point) statistics of multi-sequence
alignments. We also compute the entropic cost, that is, the loss in entropy
resulting from a constraint acting on the protein, such as the fixation of one
particular amino-acid on a specific site, and relate this notion to the escape
probability of the HIV virus. The case of lattice proteins, for which the
entropy can be computed exactly, allows us to provide another illustration of
the concept of cost, due to the competition of different folds. The relevance
of the entropy in relation to directed evolution experiments is stressed.Comment: to appear in Journal of Statistical Physic
Inverse Statistical Physics of Protein Sequences: A Key Issues Review
In the course of evolution, proteins undergo important changes in their amino
acid sequences, while their three-dimensional folded structure and their
biological function remain remarkably conserved. Thanks to modern sequencing
techniques, sequence data accumulate at unprecedented pace. This provides large
sets of so-called homologous, i.e.~evolutionarily related protein sequences, to
which methods of inverse statistical physics can be applied. Using sequence
data as the basis for the inference of Boltzmann distributions from samples of
microscopic configurations or observables, it is possible to extract
information about evolutionary constraints and thus protein function and
structure. Here we give an overview over some biologically important questions,
and how statistical-mechanics inspired modeling approaches can help to answer
them. Finally, we discuss some open questions, which we expect to be addressed
over the next years.Comment: 18 pages, 7 figure
Ab Initio Protein Structure Prediction Using Evolutionary Approach: A Survey
Protein Structure Prediction (PSP) problem is to determine the three-dimensional structure of a protein only from its primary structure. Misfolding of a protein causes human diseases. Thus, the knowledge of the structure and functionality of proteins, combined with the prediction of their structure is a complex problem and a challenge for the area of computational biology. The metaheuristic optimization algorithms are naturally applicable to support in solving NP-hard problems.These algorithms are bio-inspired, since they were designed based on procedures found in nature, such as the successful evolutionary behavior of natural systems. In this paper, we present a survey on methods to approach the \textit{ab initio} protein structure prediction based on evolutionary computing algorithms, considering both single and multi-objective optimization. An overview of the works is presented, with some details about which characteristics of the problem are considered, as well as specific points of the algorithms used. A comparison between the approaches is presented and some directions of the research field are pointed out
On the role of metaheuristic optimization in bioinformatics
Metaheuristic algorithms are employed to solve complex and large-scale optimization problems in many different fields, from transportation and smart cities to finance. This paper discusses how metaheuristic algorithms are being applied to solve different optimization problems in the area of bioinformatics. While the text provides references to many optimization problems in the area, it focuses on those that have attracted more interest from the optimization community. Among the problems analyzed, the paper discusses in more detail the molecular docking problem, the protein structure prediction, phylogenetic inference, and different string problems. In addition, references to other relevant optimization problems are also given, including those related to medical imaging or gene selection for classification. From the previous analysis, the paper generates insights on research opportunities for the Operations Research and Computer Science communities in the field of bioinformatics
- âŠ