Crowd sourcing difficult problems in protein science*

Dedicated computing resources are expensive to develop, maintain, and administrate. Frequently, research groups require bursts of computing power, during which progress is still limited by available computing resources. One way to alleviate this bottleneck would be to use additional computing resources. Today, many computing devices remain idle most of the time. Passive volunteer computing exploits this unemployed reserve of computing power by allowing device‐owners to donate computing time on their own devices. Another complementary way to alleviate bottlenecks in computing resources is to use more efficient algorithms. Engaging volunteer computing employs human intuition to help solve challenging problems for which efficient algorithms are difficult to develop or unavailable. Designing engaging volunteer computing projects is challenging but can result in high‐quality solutions. Here, we highlight four examples.     

Structure_threader: An improved method for automation and parallelization of programs structure,fastStructure and MavericK on multicore CPU systems

Structure_threader is a program to parallelize multiple runs of genetic clustering software that does not make use of multithreading technology (structure , fastStructure and MavericK) on multicore computers. Our approach was benchmarked across multiple systems and displayed great speed improvements relative to the single‐threaded implementation, scaling very close to linearly with the number of physical cores used. Structure_threader was compared to previous software written for the same task—ParallelStructure and StrAuto and was proven to be the faster (up to 25% faster) wrapper under all tested scenarios. Furthermore, Structure_threader can perform several automatic and convenient operations, assisting the user in assessing the most biologically likely value of ‘K’ via implementations such as the “Evanno,” or “Thermodynamic Integration” tests and automatically draw the “meanQ” plots (static or interactive) for each value of K (or even combined plots). Structure_threader is written in python 3 and licensed under the GPLv3. It can be downloaded free of charge at https://github.com/StuntsPT/Structure_threader .     

An autonomic provisioning framework for outsourcing data center based on virtual appliances

As outsourcing data centers emerge to host applications and services from many different organizations, it is critical for data center owners to isolate different applications while dynamically and optimally allocate sharable resources among them. To address this issue, we propose a virtual-appliance-based autonomic resource provisioning framework for large virtualized data centers. We present the architecture of the data center with enriched autonomic features. We define a non-linear constrained optimization model for dynamic resource provisioning and present a novel analytic solution. Key factors, including virtualization overhead and reconfiguration delay, are incorporated into the model. Experimental results based on a prototype demonstrate that the system-level performance has been greatly improved by taking advantage of fine-grained server consolidation, and the whole system exhibits flexible adaptation in failure scenarios. Experiments with the impact of switching delay also show the efficiency of the framework due to significantly reduced provisioning time.

Autonomic resource management in virtualized data centers using fuzzy logic-based approaches

Data centers, as resource providers, are expected to deliver on performance guarantees while optimizing resource utilization to reduce cost. Virtualization techniques provide the opportunity of consolidating multiple separately managed containers of virtual resources on underutilized physical servers. A key challenge that comes with virtualization is the simultaneous on-demand provisioning of shared physical resources to virtual containers and the management of their capacities to meet service-quality targets at the least cost. This paper proposes a two-level resource management system to dynamically allocate resources to individual virtual containers. It uses local controllers at the virtual-container level and a global controller at the resource-pool level. An important advantage of this two-level control architecture is that it allows independent controller designs for separately optimizing the performance of applications and the use of resources. Autonomic resource allocation is realized through the interaction of the local and global controllers. A novelty of the local controller designs is their use of fuzzy logic-based approaches to efficiently and robustly deal with the complexity and uncertainties of dynamically changing workloads and resource usage. The global controller determines the resource allocation based on a proposed profit model, with the goal of maximizing the total profit of the data center. Experimental results obtained through a prototype implementation demonstrate that, for the scenarios under consideration, the proposed resource management system can significantly reduce resource consumption while still achieving application performance targets.

Synthesis of a natural product-inspired eight-membered ring lactam library via ring-closing metathesis

We have prepared a novel speculative eight-membered lactam demonstration library based on the skeletal structure of the potent antitumor marine natural product octalactin A. The basic scaffold was readily constructed in a convergent fashion via ring-closing metathesis chemistry from the corresponding diene amides. A cursory examination of the biological properties of the library validates the relevance and significance of these structures.     

The interstrand amino acid pairs play a significant role in determining the parallel or antiparallel orientation of β-strands

It is widely considered that it is not appropriate to treat β-pairs in isolation, since other secondary structural models (such as helices, coils), protein topology and protein tertiary structures would limit β-strand pairing. However, to understand the underlying mechanisms of β-sheet formation, studies ought to be performed separately on more concrete aspects. In this study, we focus on the parallel or antiparallel orientation of β-strands. First, statistical analysis was performed on the relative frequencies of the interstrand amino acid pairs within parallel and antiparallel β-strands. Consequently, features were extracted by singular value decomposition from the statistical results. By using the support vector machine to distinguish the features extracted from the two types of β-strands, high accuracy was achieved (up to 99.4%). This suggests that the interstrand amino acid pairs play a significant role in determining the parallel or antiparallel orientation of β-strands. These results may provide useful information for developing other useful algorithms to examine to the β-strand folding pathways, and could eventually lead to protein structure predictions.     

An adaptive and robust biological network based on the vacant-particle transportation model

A living system reveals local computing by referring to a whole system beyond the exploration-exploitation dilemma. The slime mold, Physarum polycephalum, uses protoplasmic flow to change its own outer shape, which yields the boundary condition and forms an adaptive and robust network. This observation suggests that the whole Physarum can be represented as a local protoplasmic flow system. Here, we show that a system composed of particles, which move and are modified based upon the particle transformation that contains the relationship between the parts and the whole, can emulate the network formed by Physarum. This system balances the exploration-exploitation trade-off and shows a scale-free sub-domain. By decreasing the number of particles, our model, VP-S, can emulate the Physarum adaptive network as it is attracted to a food stimulus. By increasing the number of particles, our model, VP-D, can emulate the pattern of a growing Physarum. The patterns produced by our model were compared with those of the Physarum pattern quantitatively, which showed that both patterns balance exploration with exploitation. This model should have a wide applicability to study biological collective phenomena in general.     

Glutathione S-transferase and S-crystallins of cephalopods: Evolution from active enzyme to lens-refractive proteins

Our previous studies have shown that the S-crystallins of cephalopod (Ommastrephes sloani pacificus) eye lenses comprise a family of at least ten members which are evolutionarily related to glutathione S-transferase (GST, EC 2.5.1.18). Here we show by cDNA cloning that there are at least 24 different S-crystallins that are 46–99% identical to each other by amino acid sequence in the squid Loligo opalescens. In each species, all but one S-crystallin (SL11 in O. pacificus and Lops4 in L. opalescens) examined has an inserted central peptide of variable length and sequence. cDNA expression studies conducted in Escherichia coli showed that squid GST (which is expressed little in the lens) has very high enzymatic activity using 1-chloro-2, 4-dinitrobenzene (CDNB) as a substrate; by contrast, SL20-1 of O. pacificus and Lops 12 of L. opalescens (which are encoded by abundant lens mRNAs) have no GST activity. Interestingly, SL11 and Lops4 have some enzymatic activity with the CDNB substrate. Site-specific mutations at Y7 or W38, both residues essential for activity of vertebrate GSTs, or insertion of the central peptide present in the inactive SL20-1, reduced the specific activity of squid GST by 30- to 100-fold. These data indicate that the S-crystallins consist of a family of enzymatically inactive proteins (when using CDNB as a substrate) which is considerably larger than previously believed and that GST activity was lost by gradual drift in sequence as well as by insertion of an extra peptide by exon shuffling. The results are also consistent with the idea that SL11 and Lops4 are orthologous crystallins representing the first descendants of the ancestral GST gene in the pathway which gave rise to the extensive S-crystallin family of lens proteins. Correspondence to: S.I. Tomarev     

Malleable applications for scalable high performance computing

Iterative applications are known to run as slow as their slowest computational component. This paper introduces malleability, a new dynamic reconfiguration strategy to overcome this limitation. Malleability is the ability to dynamically change the data size and number of computational entities in an application. Malleability can be used by middleware to autonomously reconfigure an application in response to dynamic changes in resource availability in an architecture-aware manner, allowing applications to optimize the use of multiple processors and diverse memory hierarchies in heterogeneous environments. The modular Internet Operating System (IOS) was extended to reconfigure applications autonomously using malleability. Two different iterative applications were made malleable. The first is used in astronomical modeling, and representative of maximum-likelihood applications was made malleable in the SALSA programming language. The second models the diffusion of heat over a two dimensional object, and is representative of applications such as partial differential equations and some types of distributed simulations. Versions of the heat application were made malleable both in SALSA and MPI. Algorithms for concurrent data redistribution are given for each type of application. Results show that using malleability for reconfiguration is 10 to 100 times faster on the tested environments. The algorithms are also shown to be highly scalable with respect to the quantity of data involved. While previous work has shown the utility of dynamically reconfigurable applications using only computational component migration, malleability is shown to provide up to a 15% speedup over component migration alone on a dynamic cluster environment. This work is part of an ongoing research effort to enable applications to be highly reconfigurable and autonomously modifiable by middleware in order to efficiently utilize distributed environments. Grid computing environments are becoming increasingly heterogeneous and dynamic, placing new demands on applications’ adaptive behavior. This work shows that malleability is a key aspect in enabling effective dynamic reconfiguration of iterative applications in these environments.