Bahar Akbal-Delibas

Extensible and Precise Modeling for Wireless Sensor Networks

Developing applications for wireless sensor networks (WSN) is a complicated process because of the wide variety of WSN applications and low-level implementation details. Model-Driven Engineering offers an effective solution to WSN application developers by hiding the details of lower layers and raising the level of abstraction. However, balancing between abstraction level and unambiguity is challenging issue. This paper presents Baobab, a metamodeling framework for designing WSN applications and generating the corresponding code, to overcome the conflict between abstraction and reusability versus unambiguity. Baobab allows users to define functional and non-functional aspects of a system separately as software models, validate them and generate code automatically.

AN EVOLUTIONARY CONSERVATION-BASED METHOD FOR REFINING AND RERANKING PROTEIN COMPLEX STRUCTURES

Detection of protein complexes and their structures is crucial for understanding their role in the basic biology of organisms. Computational docking methods can provide researchers with a good starting point for the analysis of protein complexes. However, these methods are often not accurate and their results need to be further refined to improve interface packing. In this paper, we introduce a refinement method that incorporates evolutionary information into a novel scoring function by employing Evolutionary Trace (ET)-based scores. Our method also takes Van der Waals interactions into account to avoid atomic clashes in refined structures. We tested our method on docked candidates of eight protein complexes and the results suggest that the proposed scoring function helps bias the search toward complexes with native interactions. We show a strong correlation between evolutionary-conserved residues and correct interface packing. Our refinement method is able to produce structures with better lRMSD (least RMSD) with respect to the known complexes and lower energies than initial docked structures. It also helps to filter out false-positive complexes generated by docking methods, by detecting little or no conserved residues on false interfaces. We believe this method is a step toward better ranking and prediction of protein complexes.

GUIDING PROTEIN DOCKING WITH GEOMETRIC AND EVOLUTIONARY INFORMATION

Structural modeling of molecular assemblies promises to improve our understanding of molecular interactions and biological function. Even when focusing on modeling structures of protein dimers from knowledge of monomeric native structure, docking two rigid structures onto one another entails exploring a large configurational space. This paper presents a novel approach for docking protein molecules and elucidating native-like configurations of protein dimers. The approach makes use of geometric hashing to focus the docking of monomeric units on geometrically complementary regions through rigid-body transformations. This geometry-based approach improves the feasibility of searching the combined configurational space. The search space is narrowed even further by focusing the sought rigid-body transformations around molecular surface regions composed of amino acids with high evolutionary conservation. This condition is based on recent findings, where analysis of protein assemblies reveals that many functional interfaces are significantly conserved throughout evolution. Different search procedures are employed in this work to search the resulting narrowed configurational space. A proof-of-concept energy-guided probabilistic search procedure is also presented. Results are shown on a broad list of 18 protein dimers and additionally compared with data reported by other labs. Our analysis shows that focusing the search around evolutionary-conserved interfaces results in lower lRMSDs.

Accurate refinement of docked protein complexes using evolutionary information and deep learning

One of the major challenges for protein docking methods is to accurately discriminate native-like structures from false positives. Docking methods are often inaccurate and the results have to be refined and re-ranked to obtain native-like complexes and remove outliers. In a previous work, we introduced AccuRefiner, a machine learning based tool for refining protein-protein complexes. Given a docked complex, the refinement tool produces a small set of refined versions of the input complex, with lower root-mean-square-deviation (RMSD) of atomic positions with respect to the native structure. The method employs a unique ranking tool that accurately predicts the RMSD of docked complexes with respect to the native structure. In this work, we use a deep learning network with a similar set of features and five layers. We show that a properly trained deep learning network can accurately predict the RMSD of a docked complex with 1.40 Å error margin on average, by approximating the complex relationship between a wide set of scoring function terms and the RMSD of a docked structure. The network was trained on 35000 unbound docking complexes generated by RosettaDock. We tested our method on 25 different putative docked complexes produced also by RosettaDock for five proteins that were not included in the training data. The results demonstrate that the high accuracy of the ranking tool enables AccuRefiner to consistently choose the refinement candidates with lower RMSD values compared to the coarsely docked input structures.

An Evolutionary Conservation & Rigidity Analysis Machine Learning Approach for Detecting Critical Protein Residues

In proteins, certain amino acids may play a critical role in determining their structure and function. Examples include flexible regions which allow domain motions, and highly conserved residues on functional interfaces which play a role in binding and interaction with other proteins. Detecting these regions facilitates the analysis and simulation of protein rigidity and conformational changes, and aids in characterizing protein-protein binding. We present a machine-learning based method for the analysis and prediction of critical residues in proteins. We combine amino-acid specific information and data obtained by two complementary methods. One method, KINARI-Mutagen, performs graph-based analysis to find rigid clusters of amino acids in a protein, and the other method uses evolutionary conservation scores to find functional interfaces in proteins. We devised a machine learning model that combines both methods, in addition to amino acid type and solvent accessible surface area, to a dataset of proteins with experimentally known critical residues, and were able to achieve over 77% prediction rate, more than either of the methods separately.

Protein docking with information on evolutionary conserved interfaces

Structural modeling of molecular assemblies lies at the heart of understanding molecular interactions and biological function. We present a method for docking protein molecules and elucidating native-like structures of protein dimers. Our method is based on geometric hashing to ensure the feasibility of searching the combined conformational space of dimeric structures. The search space is narrowed by focusing the sought rigid-body transformations around surface areas with evolutionary-conserved amino-acids. Recent analysis of protein assemblies reveals that many functional interfaces are significantly conserved throughout evolution. We test our method on a broad list of sixteen diverse protein dimers and compare the structures found to have lowest lRMSD to the known native dimeric structures to those reported by other groups. Our results show that focusing the search around evolutionary-conserved interfaces results in lower lRMSDs.

A conservation and biophysics guided stochastic approach to refining docked multimeric proteins

BackgroundWe introduce a protein docking refinement method that accepts complexes consisting of any number of monomeric units. The method uses a scoring function based on a tight coupling between evolutionary conservation, geometry and physico-chemical interactions. Understanding the role of protein complexes in the basic biology of organisms heavily relies on the detection of protein complexes and their structures. Different computational docking methods are developed for this purpose, however, these methods are often not accurate and their results need to be further refined to improve the geometry and the energy of the resulting complexes. Also, despite the fact that complexes in nature often have more than two monomers, most docking methods focus on dimers since the computational complexity increases exponentially due to the addition of monomeric units.ResultsOur results show that the refinement scheme can efficiently handle complexes with more than two monomers by biasing the results towards complexes with native interactions, filtering out false positive results. Our refined complexes have better IRMSDs with respect to the known complexes and lower energies than those initial docked structures.ConclusionsEvolutionary conservation information allows us to bias our results towards possible functional interfaces, and the probabilistic selection scheme helps us to escape local energy minima. We aim to incorporate our refinement method in a larger framework which also enables docking of multimeric complexes given only monomeric structures.

Refinement of docked protein complex structures using evolutionary traces

Detection of protein complexes and their structures is crucial for understanding the role of protein complexes in the basic biology of organisms. Computational methods can provide researchers with a good starting point for the analysis of protein complexes. However, computational docking methods are often not accurate and their results need to be further refined to improve interface packing. In this paper, we introduce a novel refinement method that incorporates evolutionary information by employing an energy function containing Evolutionary Trace (ET)-based scoring function, which also takes shape complementarity, electrostatic and Van der Waals interactions into account. We tested our method on docked candidates of three protein complexes produced by a separate docking method. Our results suggest that the energy function can help biasing the results towards complexes with native interactions, filtering out false results. Our refinement method is able to produce structures with better RMSDs with respect to the known complexes and lower energies than those initial docked structures.

AccuRMSD: a machine learning approach to predicting structure similarity of docked protein complexes

Protein-protein docking methods aim to compute the correct bound form of two or more proteins. One of the major challenges for docking methods is to accurately discriminate native-like structures. The protein docking community agrees on the existence of a relationship between various favorable intermolecular interactions (e.g. Van der Waals, electrostatic, desolvation forces, etc.) and the similarity of a conformation to its native structure. Different docking algorithms often formulate this relationship as a weighted sum of selected terms and calibrate their weights against a specific training data to evaluate and rank candidate structures. However, the exact form of this relationship is unknown and the accuracy of such methods is impaired by the pervasiveness of false positives. Unlike the conventional scoring functions, we propose a novel machine learning approach that not only ranks the candidate structures relative to each other but also indicates how similar each candidate is to the native conformation. We trained the AccuRMSD neural network with an extensive dataset using the back-propagation learning algorithm and achieved RMSD prediction accuracy with less than 1Å error margin on 19,600 test samples.

Accurate Prediction of Docked Protein Structure Similarity.

One of the major challenges for protein-protein docking methods is to accurately discriminate nativelike structures. The protein docking community agrees on the existence of a relationship between various favorable intermolecular interactions (e.g. Van der Waals, electrostatic, desolvation forces, etc.) and the similarity of a conformation to its native structure. Different docking algorithms often formulate this relationship as a weighted sum of selected terms and calibrate their weights against specific training data to evaluate and rank candidate structures. However, the exact form of this relationship is unknown and the accuracy of such methods is impaired by the pervasiveness of false positives. Unlike the conventional scoring functions, we propose a novel machine learning approach that not only ranks the candidate structures relative to each other but also indicates how similar each candidate is to the native conformation. We trained the AccuRMSD neural network with an extensive dataset using the back-propagation learning algorithm. Our method achieved predicting RMSDs of unbound docked complexes with 0.4Å error margin.