Irina Hashmi

An Efficient Design of a Reversible Barrel Shifter

The key objective of today’s circuit design is to increase the performance without the proportional increase in power consumption. In this regard, reversible logic has become an immensely promising technology in the field of low power computing and designing. On the other hand, data shifting and rotating are required in many operations such as arithmetic and logical operations, address decoding and indexing etc. In this consequence, barrel shifters, which can shift and rotate multiple bits in a single cycle, have become a common design choice for high speed applications. For this reason, this paper presents an efficient design of a reversible barrel shifter. It has also been shown that the new circuit outperforms the previously proposed one in terms of number of gates, number of garbage outputs, delay and quantum cost.

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.

Basin hopping as a general and versatile optimization framework for the characterization of biological macromolecules

Since its introduction, the basin hopping (BH) framework has proven useful for hard nonlinear optimization problems with multiple variables and modalities. Applications span a wide range, from packing problems in geometry to characterization of molecular states in statistical physics. BH is seeing a reemergence in computational structural biology due to its ability to obtain a coarse-grained representation of the protein energy surface in terms of local minima. In this paper, we show that the BH framework is general and versatile, allowing to address problems related to the characterization of protein structure, assembly, and motion due to its fundamental ability to sample minima in a high-dimensional variable space. We show how specific implementations of the main components in BH yield algorithmic realizations that attain state-of-the-art results in the context of ab initio protein structure prediction and rigid protein-protein docking. We also show that BH can map intermediate minima related with motions connecting diverse stable functionally relevant states in a protein molecule, thus serving as a first step towards the characterization of transition trajectories connecting these states.

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.

HopDock: a probabilistic search algorithm for decoy sampling in protein-protein docking

BackgroundElucidating the three-dimensional structure of a higher-order molecular assembly formed by interacting molecular units, a problem commonly known as docking, is central to unraveling the molecular basis of cellular activities. Though protein assemblies are ubiquitous in the cell, it is currently challenging to predict the native structure of a protein assembly in silico.MethodsThis work proposes HopDock, a novel search algorithm for protein-protein docking. HopDock efficiently obtains an ensemble of low-energy dimeric configurations, also known as decoys, that can be effectively used by ab-initio docking protocols. HopDock is based on the Basin Hopping (BH) framework which perturbs the structure of a dimeric configuration and then follows it up with an energy minimization to explicitly sample a local minimum of a chosen energy function. This process is repeated in order to sample consecutive energy minima in a trajectory-like fashion. HopDock employs both geometry and evolutionary conservation analysis to narrow down the interaction search space of interest for the purpose of efficiently obtaining a diverse decoy ensemble.Results and conclusionsA detailed analysis and a comparative study on seventeen different dimers shows HopDock obtains a broad view of the energy surface near the native dimeric structure and samples many near-native configurations. The results show that HopDock has high sampling capability and can be employed to effectively obtain a large and diverse ensemble of decoy configurations that can then be further refined in greater structural detail in ab-initio docking protocols.

A basin hopping algorithm for protein-protein docking

We present a novel probabilistic search algorithm to efficiently search the structure space of protein dimers. The algorithm is based on the basin hopping framework that repeatedly follows up structural perturbation with energy minimization to obtain a coarse-grained view of the dimeric energy surface in terms of its local minima. A Metropolis criterion biases the search towards lower-energy minima over time. Extensive analysis highlights efficient and effective implementations for the perturbation and minimization components. Testing on a broad list of dimers shows the algorithm recovers the native dimeric configuration with great accuracy and produces many minima near the native configuration. The algorithm can be employed to efficiently produce relevant decoys that can be further refined at greater detail to predict the native configuration.

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.

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.

idDock+: Integrating Machine Learning in Probabilistic Search for Protein–Protein Docking

Predicting the three-dimensional native structures of protein dimers, a problem known as protein-protein docking, is key to understanding molecular interactions. Docking is a computationally challenging problem due to the diversity of interactions and the high dimensionality of the configuration space. Existing methods draw configurations systematically or at random from the configuration space. The inaccuracy of scoring functions used to evaluate drawn configurations presents additional challenges. Evidence is growing that optimization of a scoring function is an effective technique only once the drawn configuration is sufficiently similar to the native structure. Therefore, in this article we present a method that employs optimization of a sophisticated energy function, FoldX, only to locally improve a promising configuration. The main question of how promising configurations are identified is addressed through a machine learning method trained a priori on an extensive dataset of functionally diverse protein dimers. To deal with the vast configuration space, a probabilistic search algorithm operates on top of the learner, feeding to it configurations drawn at random. We refer to our method as idDock+, for informatics-driven Docking. idDock+is tested on 15 dimers of different sizes and functional classes. Analysis shows that on all systems idDock+finds a near-native structure and is comparable in accuracy to other state-of-the-art methods. idDock+ represents one of the first highly efficient hybrid methods that combines fast machine learning models with demanding optimization of sophisticated energy scoring functions. Our results indicate that this is a promising direction to improve both efficiency and accuracy in docking.

Informatics-driven Protein-protein Docking

Predicting the structure of protein assemblies is fundamental to our ability to understand the molecular basis of biological function. The basic protein-protein docking problem involving two protein units docking onto each-other remains challenging. One direction of research is exploring probabilistic search algorithms with high exploration capability, but these algorithms are limited by errors in current energy functions. A complementary direction is choosing to understand what constitutes true interaction interfaces. In this paper we present a method that combines the two directions and advances research into computationally-efficient yet high-accuracy docking. We present an informatics-driven probabilistic search algorithm for rigid protein-protein docking. The algorithm builds upon the powerful basin hopping framework, which we have shown in many settings in molecular modeling to have high exploration capability. Rather than operate de novo, the algorithm employs information on what constitutes a native interaction interface. A predictive machine learning model is built and trained a priori on known dimeric structures to learn features correlated with a true interface. The model is fast, accurate, and replaces expensive physics-based energy functions in scoring sampled configurations. A sophisticated energy function is used to refine only high-scoring configurations. The result is an ensemble of high-quality decoy configurations that we show here to approach the known native dimeric structure better than other state-of-the-art docking methods. We believe the proposed method advances computationally-efficient high-accuracy docking.