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1

Dissimilar sequence: similar structure of proteins

Banach M., Konieczny L., Roterman I.

Bio-Algorithms and Med-Systems

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2016

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Vol. 12, no. 3

117--121

EN

Sequence-to-structure relation is one of the major objects of the analysis of protein folding problem. The pair of two small proteins (domains) of similar structure (β-hairpin/α-helix/β-hairpin) generated by the chains of similar length (about 60 amino acids) with very low sequence similarity (15%) is the object of the comparable analysis of 3D structure. The criterion for similarity estimation is the status of polypeptide chain with respect to the hydrophobic core structure. The fuzzy oil drop model is applied to reveal the differentiated status of fragments of the well-defined secondary structure. This analysis allows the interpretation of the structure in other than the geometric form as it is made based on secondary structure classification. The two compared highly similar proteins appear to be different with respect to the hydrophobic core structure.

2

Artificial Bee Colony Algorithm for the Protein Structure Prediction Based on the Toy Model

Li Y., Zhou C., Zheng X.

Fundamenta Informaticae

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2015

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Vol. 136, nr 3

241--252

EN

The protein structure folding is one of the most challenging problems in the field of bioinformatics. The main problem of protein structure prediction in the 3D toy model is to find the lowest energy conformation. Although many heuristic algorithms have been proposed to solve the protein structure prediction (PSP) problem, the existing algorithms are far from perfect since PSP is an NP-problem. In this paper, we proposed an artificial bee colony (ABC) algorithm based on the toy model to solve PSP problem. In order to improve the global convergence ability and convergence speed of the ABC algorithm, we adopt a new search strategy by combining the global solution into the search equation. Experimental results illustrate that the suggested algorithm can get the lowest energy when the algorithm is applied to the Fibonacci sequences and to four real protein sequences which come from the Protein Data Bank (PDB). Compared with the results obtained by PSO, LPSO, PSO-TS, PGATS, our algorithm is more efficient.

3

Is the growth rate of Protein Data Bank sufficient to solve the protein structure prediction problem using template-based modeling?

Bryliński M.

Bio-Algorithms and Med-Systems

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2015

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Vol. 11, no. 1

1--7

EN

The Protein Data Bank (PDB) undergoes an exponential expansion in terms of the number of macromolecular structures deposited every year. A pivotal question is how this rapid growth of structural information improves the quality of three-dimensional models constructed by contemporary bioinformatics approaches. To address this problem, we performed a retrospective analysis of the structural coverage of a representative set of proteins using remote homology detected by COMPASS and HHpred. We show that the number of proteins whose structures can be confidently predicted increased during a 9-year period between 2005 and 2014 on account of the PDB growth alone. Nevertheless, this encouraging trend slowed down noticeably around the year 2008 and has yielded insignificant improvements ever since. At the current pace, it is unlikely that the protein structure prediction problem will be solved in the near future using existing template-based modeling techniques. Therefore, further advances in experimental structure determination, qualitatively better approaches in fold recognition, and more accurate template-free structure prediction methods are desperately needed.

4

Improving protein structure prediction, refinement and quality assessment techniques

Leelananda S. P., Pawłowski M., Faraggi E., Kloczkowski A.

TASK Quarterly : scientific bulletin of Academic Computer Centre in Gdansk

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2014

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Vol. 18, No 3

231--243

EN

Several novel techniques have been combined to improve protein structure prediction, structural refinement and quality assessment of protein models. We discuss in brief the development of four-body potentials that take into account dense packing and cooperativity of interactions of proteins, and its success. We have developed a metho d that uses whole protein information filtered through machine learning to score protein models base d on their likeness to native structure. Here we consider electrostatic interactions and residue depth, and use these for structure prediction. These potentials were tested to be succe ssful in CASP 9 and CASP 10. We have also developed a Quality Assessment technique, MQAP single, which is a quasi-single-model MQAP , by combining advantages of both “pure” single-model MQAP s and clustering MQAP s. This technique can be used in ranking and assessing the absolute global quality of single protein models. This model (Pawlowski-Kloczkowski) was ranked 3rd in Model Quality Assessment in CASP 10. Consideration of protein flexibility and its fluctuation dynamics improves protein structure prediction and leads to better refinement of computational models of proteins. Here we also discuss how Anisotropic Network Model ( ANM ) of protein fluctuation dynamics and Go-like model of energy score can be used for novel protein structure refinement.

5

Implementation and evaluation of new protocol for comparative modeling of protein structures

Strumillo M., Dawid A. E., Szczasiuk A., Gront D.

TASK Quarterly : scientific bulletin of Academic Computer Centre in Gdansk

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2014

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Vol. 18, No 4

379--384

EN

Template-based modeling (termed also Comparative or Homology Modeling) of a protein structure is one of ubiquitous tasks of structural bioinfor matics. The method can deliver model structures important for testing biological hypotheses, virtual docking and drug design. The performance of these methods is evaluated every two years during a Critical Assessment of Protein Structure Prediction (CASP) experiment. In this contribution we present a new automated protocol for template-base d modeling, which combines computational tools recently developed in our laboratory: the dat abase of protein domain structures (BDDB) with one dimensional and three dimensional thread ing applications. The protocol was tested during a CASP11 experiment.

6

A new approach to homology modeling

He Y., Scheraga H. A., Rackovsky S.

TASK Quarterly : scientific bulletin of Academic Computer Centre in Gdansk

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2014

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Vol. 18, No 3

211--128

EN

The need to interpret experimental results led to, first, an all-atom f orce field, followed by a coarse-grained one. As an aid to these force fields, a new approac h is introduced here to predict protein structure based on the physical properties of th e amino acids. This approach includes three key components: Kidera factors describing the ph ysical properties, Fourier transformation and UNRES coarse-grained force field simulations. Different from traditional homology modeling methods which are based on evolution, this approach is phys ics-based, and does not have the same weaknesses as the traditional homology modeling method s. Our results show that this approach can produce above average prediction results, and can be used as a useful tool for protein structure prediction.

7

Smotifs as structural local descriptors of supersecondary elements: classification, completeness and applications

Bonet J., Fiser A., Oliva B., Fernandez-Fuentes N.

Bio-Algorithms and Med-Systems

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2014

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Vol. 10, no. 4

195--212

EN

Protein structures are made up of periodic and aperiodic structural elements (i.e., α-helices, β-strands and loops). Despite the apparent lack of regular structure, loops have specific conformations and play a central role in the folding, dynamics, and function of proteins. In this article, we reviewed our previous works in the study of protein loops as local supersecondary structural motifs or Smotifs. We reexamined our works about the structural classification of loops (ArchDB) and its application to loop structure prediction (ArchPRED), including the assessment of the limits of knowledge-based loop structure prediction methods. We finalized this article by focusing on the modular nature of proteins and how the concept of Smotifs provides a convenient and practical approach to decompose proteins into strings of concatenated Smotifs and how can this be used in computational protein design and protein structure prediction.

8

Opportunities and limitations in applying coevolution-derived contacts to protein structure prediction

Tetchner S., Kosciolek T., Jones D. T.

Bio-Algorithms and Med-Systems

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2014

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Vol. 10, no. 4

243--254

EN

The prospect of identifying contacts in protein structures purely from aligned protein sequences has lured researchers for a long time, but progress has been modest until recently. Here, we reviewed the most successful methods for identifying structural contacts from sequence and how these methods differ and made an initial assessment of the overlap of predicted contacts by alternative approaches. We then discussed the limitations of these methods and possibilities for future development and highlighted the recent applications of contacts in tertiary structure prediction, identifying the residues at the interfaces of protein-protein interactions, and the use of these methods in disentangling alternative conformational states. Finally, we identified the current challenges in the field of contact prediction, concentrating on the limitations imposed by available data, dependencies on the sequence alignments, and possible future developments.

9

Exact methods for lattice protein models

Mann M., Backofen R.

Bio-Algorithms and Med-Systems

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2014

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Vol. 10, no. 4

213--225

EN

Lattice protein models are well-studied abstractions of globular proteins. By discretizing the structure space and simplifying the energy model over regular proteins, they enable detailed studies of protein structure formation and evolution. However, even in the simplest lattice protein models, the prediction of optimal structures is computationally difficult. Therefore, often, heuristic approaches are applied to find such conformations. Commonly, heuristic methods find only locally optimal solutions. Nevertheless, there exist methods that guarantee to predict globally optimal structures. Currently, only one such exact approach is publicly available, namely the constraint-based protein structure prediction method and variants. Here, we review exact approaches and derived methods. We discuss fundamental concepts like hydrophobic core construction and their use in optimal structure prediction, as well as possible applications like combinations of different energy models.

10

3D-Judge : a metaserver approach to protein structure prediction

Jaśkowski W., Błażewicz J., Łukasiak P., Miłostan M., Krasnogor N.

Foundations of Computing and Decision Sciences

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2007

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Vol. 32, No. 1

3-14

EN

Analysing arid predicting the detailed three dimensional conformation of protein structures is a critical and important task within structural bioinformatics with impact on other fields, e.g.. drug design and delivery, sensing technologies, etc. Unfortunately, it is hard to identify one methodology that will give the best prediction of the three-dimensional structure for any sequence. That is, some predictors are best suited for some sequences and not for others. In trying to address this drawback of current prediction algorithms the research community introduced the concept of protein prediction metaservers. In this paper we propose a new metaserver method called 3D-Judge that uses an artificial neural network (ANN) to select the best model from among models produced by individual servers. The fundamental innovation we introduce is that the AXN is not only used to decide which models and servers to use as good predictions but, crucially, it is also used to analyse and "remember" the past performances of the servers it has access to. Thus, our method acts as both a kind of majority voting algorithm, by selecting models arising from different servers based on their mutual similarity, and also a reinforced learning method that takes cues from historical data of previously solved structures. We train and evaluate our metaserver based on previous GASP results and we compare SD-Judge with a popular and effective metaserver, namely. 3D-Jury. The obtained results indicate that 3D-Judge is competitive with 3D-Jury, outperforming it on many cases. We also present a discussion on future extensions to 3D-Judge.

11

Structure of Proteins : New Approach to Molecular Modeling

Koliński A., Rotkiewicz P., Skolnick J.

Polish Journal of Chemistry

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2001

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Vol. 75, nr 4

587-599

EN

The force field and Monte Carlo sampling method of our recently developed reduced model of proteins is described. Recent applications of the models include ab initio structure prediction for small globular proteins, modeling of protein structure based on distantly homologous (or analogous) structural templates, assembly of protein structure from sparse experimental data, and computational studies of protein folding dynamics and thermodynamics. The newest application, described in this paper, enables the prediction of low-to-moderate resolution coordinates of the parts of protein structure that are missed in incomplete PDB files.