Jonathan E. Kohn, Ian S. Millett, Jaby Jacob, Bojan Zagrovic, Thomas M. Dillon, Nikolina Cingel, Robin S. Dothager, Soenke Seifert, P. Thiyagarajan, Tobin R. Sosnick, M. Zahid Hasan, Vijay S. Pande, Ingo Ruczinski, Sebastian Doniach, Kevin W. Plaxco, and Robert L. Baldwin
Proceedings of the National Academy of Sciences of the United States of America. 101(34):12491-12496
REINFORCEMENT learning, NUCLEOTIDE sequence, BIOENGINEERING, COMPUTER science, and COMPUTATIONAL biology
We use reinforcement learning to train an agent for computational RNA design: given a target secondary structure, design a sequence that folds to that structure in silico. Our agent uses a novel graph convolutional architecture allowing a single model to be applied to arbitrary target structures of any length. After training it on randomly generated targets, we test it on the Eterna100 benchmark and find it outperforms all previous algorithms. Analysis of its solutions shows it has successfully learned some advanced strategies identified by players of the game Eterna, allowing it to solve some very difficult structures. On the other hand, it has failed to learn other strategies, possibly because they were not required for the targets in the training set. This suggests the possibility that future improvements to the training protocol may yield further gains in performance. [ABSTRACT FROM AUTHOR]
Eastman, Peter, Swails, Jason, Chodera, John D., McGibbon, Robert T., Zhao, Yutong, Beauchamp, Kyle A., Wang, Lee-Ping, Simmonett, Andrew C., Harrigan, Matthew P., Stern, Chaya D., Wiewiora, Rafal P., Brooks, Bernard R., and Pande, Vijay S.
MOLECULAR dynamics, COMPUTER software, ALGORITHMS, COMPUTER-assisted molecular modeling, and CODING theory
OpenMM is a molecular dynamics simulation toolkit with a unique focus on extensibility. It allows users to easily add new features, including forces with novel functional forms, new integration algorithms, and new simulation protocols. Those features automatically work on all supported hardware types (including both CPUs and GPUs) and perform well on all of them. In many cases they require minimal coding, just a mathematical description of the desired function. They also require no modification to OpenMM itself and can be distributed independently of OpenMM. This makes it an ideal tool for researchers developing new simulation methods, and also allows those new methods to be immediately available to the larger community. [ABSTRACT FROM AUTHOR]
Chang, Huang-Wei, Bacallado, Sergio, Pande, Vijay S., and Carlsson, Gunnar E.
PLoS ONE; Apr2013, Vol. 8 Issue 4, p1-10, 10p
TOPOLOGY, MOLECULAR simulation methods, ALGORITHMS, PERTURBATION theory, DATA mining, SIGNAL processing, PARAMETER estimation, and INFORMATION theory
The large amount of molecular dynamics simulation data produced by modern computational models brings big opportunities and challenges to researchers. Clustering algorithms play an important role in understanding biomolecular kinetics from the simulation data, especially under the Markov state model framework. However, the ruggedness of the free energy landscape in a biomolecular system makes common clustering algorithms very sensitive to perturbations of the data. Here, we introduce a data-exploratory tool which provides an overview of the clustering structure under different parameters. The proposed Multi-Persistent Clustering analysis combines insights from recent studies on the dynamics of systems with dominant metastable states with the concept of multi-dimensional persistence in computational topology. We propose to explore the clustering structure of the data based on its persistence on scale and density. The analysis provides a systematic way to discover clusters that are robust to perturbations of the data. The dominant states of the system can be chosen with confidence. For the clusters on the borderline, the user can choose to do more simulation or make a decision based on their structural characteristics. Furthermore, our multi-resolution analysis gives users information about the relative potential of the clusters and their hierarchical relationship. The effectiveness of the proposed method is illustrated in three biomolecules: alanine dipeptide, Villin headpiece, and the FiP35 WW domain. [ABSTRACT FROM AUTHOR]
PROTEOMICS, SALIVARY proteins, ANOPHELES, MOSQUITO vectors, and PROTEIN expression
In order to understand the importance of functional proteins in mosquito behavior, following blood meal, a baseline proteomic dataset is essential for providing insights into the physiology of blood feeding. Therefore, in this study as first step, in solution and 1-D electrophoresis digestion approach combined with tandem mass spectrometry (nano LC-MS/MS) and computational bioinformatics for data mining was used to prepare a baseline proteomic catalogue of salivary gland proteins of sugar fed An. culicifacies mosquitoes. A total of 106 proteins were identified and analyzed by SEQUEST algorithm against mosquito protein database from Uniprot/NCBI. Importantly, D7r1, D7r2, D7r4, salivary apyrase, anti-platelet protein, calreticulin, antigen 5 family proteins were identified and grouped on the basis of biological and functional roles. Secondly, differential protein expression and annotations between salivary glands of sugar fed vs blood fed mosquitoes was analyzed using 2-Delectrophoresis combined with MALDI-TOF mass spectrometry. The alterations in the differential expression of total 38 proteins was observed out of which 29 proteins like beclin-1, phosphorylating proteins, heme oxygenase 1, ferritin, apoptotic proteins, coagulation and immunity like, serine proteases, serpins, c-type lectin and protein in regulation of blood feeding behavior were found to be up regulated while 9 proteins related to blood feeding, juvenile hormone epoxide hydrolase ii, odorant binding proteins and energy metabolic enzymes were found to be down regulated. To our knowledge, this study provides a first time baseline proteomic dataset and functional annotations of An. culicifacies salivary gland proteins that may be involved during the blood feeding. Identification of differential salivary proteins between sugar fed and blood fed mosquitoes and their plausible role may provide insights into the physiological processes associated with feeding behavior and sporozoite transmission during the process of blood feeding. [ABSTRACT FROM AUTHOR]
Stockholms universitet, Naturvetenskapliga fakulteten, Institutionen för biokemi och biofysik, Kasson, Peter M, Lindahl, Erik, and Pande, Vijay S
PloS Computational Biology. 6(6)
Natural Sciences, Chemical Sciences, Theoretical Chemistry, Naturvetenskap, Kemi, Teoretisk kemi, Biological Sciences, Biochemistry and Molecular Biology, Biologiska vetenskaper, Biokemi och molekylärbiologi, NATURAL SCIENCES, Chemistry, Biochemistry, NATURVETENSKAP, and Biokemi
Membrane fusion is essential to both cellular vesicle trafficking and infection by enveloped viruses. While the fusion protein assemblies that catalyze fusion are readily identifiable, the specific activities of the proteins involved and nature of the membrane changes they induce remain unknown. Here, we use many atomic-resolution simulations of vesicle fusion to examine the molecular mechanisms for fusion in detail. We employ committor analysis for these million-atom vesicle fusion simulations to identify a transition state for fusion stalk formation. In our simulations, this transition state occurs when the bulk properties of each lipid bilayer remain in a lamellar state but a few hydrophobic tails bulge into the hydrophilic interface layer and make contact to nucleate a stalk. Additional simulations of influenza fusion peptides in lipid bilayers show that the peptides promote similar local protrusion of lipid tails. Comparing these two sets of simulations, we obtain a common set of structural changes between the transition state for stalk formation and the local environment of peptides known to catalyze fusion. Our results thus suggest that the specific molecular properties of individual lipids are highly important to vesicle fusion and yield an explicit structural model that could help explain the mechanism of catalysis by fusion proteins.