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1

A multi-objective cluster-based biased random-key genetic algorithm with online parameter control applied to protein structure prediction

Marchi Felipe, Stubs Parpinelli Rafael

Annals of Computer Science and Information Systems

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2022

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Vol. 30

337--346

EN

The protein structure prediction problem is one of the most important bioinformatics problems. Computational methods can be used to approach this problem and de novo methods are able to generate protein structures without the need of having known similar structures to the predicted protein. These methods transform the prediction problem into an optimization problem, using optimization models that combine different energy functions and high-level information. These models usually have only a single optimization objective. However, it is known that this single objective optimization approach may harm the optimization search due to the existence of conflicts between the different terms that compose the optimization objective. The proposed model has three objectives: energy function, secondary structure, and contact maps. A multi-objective Biased Random-Key Genetic Algorithm (BRKGA) with online parameter control, named MOBO, is proposed as the optimizer. The final predictor comprises two phases of the MOBO algorithm and selects a final structure using the MUFOLD-CL clustering method. Results obtained demonstrated that the proposed predictor generated highly competitive results with the literature.

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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.

3

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.

4

Struktura, dynamika i termodynamika białek globularnych: siatkowe modele Monte Carlo

Koliński A.

Polimery

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1998

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T. 43, nr 6

341-351

PL

Scharakteryzowano własny, opisany już w literaturze symulacyjny model białka, oparty na dyskretyzacji siatkowej i metodzie Monte Carlo. Oddziaływania pomiędzy tzw. atomami połączonymi wyprowadzono w nim w postaci potencjałów statystycznych wynikających z analizy regularności strukturalnych obserwowanych w znanych strukturach jednodomenowych białek globularnych. Przedstawiono kilka przykładowych zastosowań i przedyskutowano zakres stosowalności modelu oraz kierunki jego dalszego rozwoju. Bardziej szczegółowo opisano wyniki symulacji procesu asocjacji podwójnej helisy fragmentu białka transkrypcyjnego GCN4

EN

A Monte Carlo high-resolution lattice model of protein structure and dynamics is described. Interactions between combined atoms are described in terms of statistical potentials derived from the analysis of the structural regularities occurring in the known structures of single-domain globular proteins. Illustrative applications, applicability range, and possible further development of the model are given. Results of the simulations of the coil assembly of the leucine zipper fragment of transcriptional activator GCN4 are discussed in more detail.