Full-Text RSS V3.2
Argimiro Krishnamoorthy
We are pleased to announce the new release of Tetra Data Platform (TDP) v3.2, providing a collection of new capabilities across all areas of the platform as well as improvements and enhancements to the user experience.
Life sciences companies have a greater need than ever to use data to provide insights and direction for drug discovery. Unfortunately, these companies face several common challenges ranging from slow and manual collection and processing of data, being unable to find data scattered across disconnected silos, and difficulty maintaining clean and compliant data. These challenges directly impact an organization's productivity.
TDP v3.2 addresses these needs through new capabilities that provide greater flexibility managing the pipelines that process data, more powerful and easier-to-use search, and improved governance and auditability. These capabilities enable organizations to achieve more with their scientific data.
The new API-first capabilities allow you to create and check the status of pipelines directly from the API. You also gain the scalability and flexibility to seamlessly integrate and customize your data processing pipelines for existing business processes and workflows, reducing complexity and cost. With this programmatic pipeline configuration, it is now easier than ever to incorporate new technologies or scale existing ones.
Additionally, with the added ability to programmatically update Tetra File-Log Agent configuration JSON, teams can quickly configure and deploy new agents across the organization as well as check status and make updates to existing configurations.
TDP 3.2 provides several new features that make it faster to find and access the data you need. An intuitive, full-text search allows you to find data using a query syntax similar to a familiar website search. An improved, relevance-ranked search provides the most meaningful search results right at the top of the list.
TDP v3.2 also provides improved file exploration through saved searches and shortcuts. Retrieve data from a previous search or find critical data instantly without having to explore across the entire file path. Additionally, new file preview lets you browse files and view their content directly from the TDP search results interface without needing to download or open with a dedicated application.
Administrators gain greater control managing user access through multi-organization support. This ability provides improved security and flexibility by allowing a set of system administrators to manage multiple projects, teams, and organizations all from within a single TDP instance.
We also continue to enhance our audit trail capabilities with greater detail for the Tetra File-Log Agent, improving visibility for debug and diagnosis, reducing the time and support needed for audits, and strengthening 21 CFR Part 11 compliance.
Beyond these new features, TDP v3.2 comes with many new user experience improvements, enhancements, and fixes across all areas of the product. We have also begun updating the user interface, which will continue throughout future releases, making common tasks easier and more intuitive. To learn about all of the updates and benefits provided by Tetra Data Platform v3.2, please check out the TDP v3.2 Release Notes.
Speech to text REST API v3.2 is available in preview.Speech to text REST API v3.1 is generally available.Speech to text REST API v3.0 will be retired on April 1st, 2026. For more information, see the Speech to text REST API v3.0 to v3.1 and v3.1 to v3.2 migration guides.
Copyright: 2011 Chaudhury et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: This work was supported by National Institutes of Health grant R01 GM078221 ( ). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
The formation of highly specific protein complexes is a fundamental process in biology, and the structures of these complexes can yield deep insight into the mechanisms of protein function. Computational protein docking provides a means by which to predict the structure of protein-protein complexes from their unbound structures. Blind structure-prediction efforts, such as the Critical Assessment of Protein Interactions (CAPRI) [1], [2] have showcased a number of successful docking strategies using a range of methods from course-grained fast-Fourier transform approaches which identify surface complementarity between two partners [3], [4] to all-atom stochastic methods that can accommodate intricate protein conformational changes [5], [6]. In a number of CAPRI strategies, [3], [7], [8], [9], [10] as well as other protein docking studies [11], [12], the protein docking component of the Rosetta v2 software package, RosettaDock [13], has proved useful for a range of protein docking applications.
RosettaDock was first introduced as a multi-scale Monte Carlo based docking algorithm that utilized a centroid-based coarse grain stage to quickly identify favorable docking poses and an all-atom refinement stage that simultaneously optimized rigid-body position and side-chain conformation. Since then RosettaDock has been modified to address the critical challenge in protein-protein docking: binding-induced backbone conformational changes. Wang et al. introduced explicit loop modeling and backbone minimization [6] while we added ensemble-based docking [14] and conformational move sets specific to antibody docking [15]. In that span, RosettaDock has been used for a wide range of applications from antibody-antigen docking [11], [12], to peptide docking and specificity [16], [17] to multi-body [18] and symmetric docking.[19]
The current version of Rosetta, v3.2, has been in development for the past two years. The original Rosetta software package was written primarily for ab initio protein folding [20] but quickly expanded to include an array of molecular modeling applications from protein docking to enzyme design. The new Rosetta software package [21] was written from the ground up with these diverse applications in mind. Essential components such as energy function calculators, protein structure objects, and chemical parameters were assembled into common software layers accessible to all protocols. Protocols such as side-chain packing, or energy minimization, were written with a modular object-oriented architecture that allows users and programmers to easily combine different molecular modeling objects and functions. Control objects were written to give users a generalized scheme from which to precisely specify the sampling strategy for a given protocol. Finally, user interfaces such as RosettaScripts,[22] PyRosetta [23], and a PyMol interface [24] were developed to provide unprecedented accessibility of the code.
The protein docking component of Rosetta v3.2, was written with two main goals. The first goal was to include all the core docking capabilities of Rosetta v2.3. The second, take advantage of the modular Rosetta v3.2 architecture to easily include new features such as modeling small-molecules, [25] noncanonical amino acids, and post-translational modifications, adding more customized conformational constraints, or allowing for alternative side-chain packing or design schemes. In order to systematically evaluate docking performance, we ran both RosettaDock v2.3 and RosettaDock v3.2 against the recently expanded Protein Docking Benchmark 3.0 [26]. The results of this benchmark can determine whether RosettaDock v3.2 successfully reproduces or improves upon the results of RosettaDock v2.3. More importantly, benchmarking identifies the strengths and weakness of the core RosettaDock algorithm against a large diverse set of targets to guide future development.
Finally, in order to showcase the additional capabilities of the Rosetta v3 software package, we identified a subset of targets in the benchmark that contain small-molecule co-factors in or near the binding site. Although these co-factors are critical to biological protein function and interactions, due to their non-protein nature, they are often excluded from many docking algorithms, including Rosetta v2.3. We utilize the small-molecule modeling components of Rosetta v3.2 to incorporate these co-factors in the docking process to test whether performance would improve.
Once the centroid-mode stage is complete, the lowest energy structure accessed during that stage is selected for high-resolution refinement. During high-resolution refinement, centroid pseudo-atoms are replaced with the side-chain atoms at their initial unbound conformations. Then 50 MC steps are made in which the (1) rigid-body position is perturbed by a random direction and magnitude specified by a Gaussian distribution around 0.1 and 3.0, (2) the rigid-body orientation is energy-minimized, and (3) side-chain conformations are optimized with RotamerTrials [27], followed by a test of the Metropolis criteria. Every eight steps, an additional combinatorial side-chain optimization is carried out using the full side-chain packing algorithm, followed by an additional Metropolis criteria check. To reduce the time devoted to the computationally expensive energy-minimization for unproductive rigid-body moves, minimization is skipped if a rigid-body move results in a change in score of greater than +15. The all-atom score function used in this stage primarily consists of Van der Waals attractive and repulsive terms, a solvation term, an explicit hydrogen bonding term, a statistical residue-residue pair-wise interaction term, an internal side-chain conformational energy term, and an electrostatic term (Table 1) [13].
For particular targets, a variety of RosettaDock sampling strategies are often used to improve the chance of achieving an accurate structure prediction [28]. If no prior structural or biochemical information is known about the protein interaction of interest, global docking is used to randomize the initial docking poses. From there, score filters and clustering are used to identify clusters of acceptable low-energy structures for further docking and refinement. In most cases, there is some known information about the complex, either in the form of related protein complexes or in biochemical or bioinformatics data which identify probable regions of interaction on the protein partners. In these cases users manually arrange the starting docking pose to a configuration that is compatible with the information and carry out a local docking perturbation. Additionally, users can set distance-based filters that bias sampling towards those docking poses that are compatible with specified constraints [28]. If backbone conformational changes are anticipated, appropriate backbone sampling strategies are prescribed [6], [8], [14], [15].
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