Ruben Acuña

Protein intrachain contact prediction with most interacting residues (MIR)

Abstract The transition state ensemble during the folding process of globular proteins occurs when a sufficient number of intrachain contacts are formed, mainly, but not exclusively, due to hydrophobic interactions. These contacts are related to the folding nucleus, and they contribute to the stability of the native structure, although they may disappear after the energetic barrier of transition states has been passed. A number of structure and sequence analyses, as well as protein engineering studies, have shown that the signature of the folding nucleus is surprisingly present in the native three-dimensional structure, in the form of closed loops, and also in the early folding events. These findings support the idea that the residues of the folding nucleus become buried in the very first folding events, therefore helping the formation of closed loops that act as anchor structures, speed up the process, and overcome the Levinthal paradox. We present here a review of an algorithm intended to simulate in a discrete space the early steps of the folding process. It is based on a Monte Carlo simulation where perturbations, or moves, are randomly applied to residues within a sequence. In contrast with many technically similar approaches, this model does not intend to fold the protein but to calculate the number of non-covalent neighbors of each residue, during the early steps of the folding process. Amino acids along the sequence are categorized as most interacting residues (MIRs) or least interacting residues. The MIR method can be applied under a variety of circumstances. In the cases tested thus far, MIR has successfully identified the exact residue whose mutation causes a switch in conformation. This follows with the idea that MIR identifies residues that are important in the folding process. Most MIR positions correspond to hydrophobic residues; correspondingly, MIRs have zero or very low accessible surface area. Alongside the review of the MIR method, we present a new postprocessing method called smoothed MIR (SMIR), which refines the original MIR method by exploiting the knowledge of residue hydrophobicity. We review known results and present new ones, focusing on the ability of MIR to predict structural changes, secondary structure, and the improved precision with the SMIR method.

Managing and Documenting Legacy Scientific Workflows

Scientific legacy workflows are often developed over many years, poorly documented and implemented with scripting languages. In the context of our cross-disciplinary projects we face the problem of maintaining such scientific workflows. This paper presents the Workflow Instrumentation for Structure Extraction (WISE) method used to process several ad-hoc legacy workflows written in Python and automatically produce their workflow structural skeleton. Unlike many existing methods, WISE does not assume input workflows to be preprocessed in a known workflow formalism. It is also able to identify and analyze calls to external tools. We present the method and report its results on several scientific workflows.

Instrumentation and Trace Analysis for Ad-Hoc Python Workflows in Cloud Environments

Knowledge of structure is critical to map legacy workflows to environments suitable to run on the cloud. We present a method which characterizes a workflow structure with the execution trace produced by instrumented logging functionality. The method generates the structure of workflows to support their reuse by permitting their transformation into modern execution environments. The method presented in the paper is implemented for Python workflows and demonstrated in the context of several legacy scientific workflows.

A workflow for the prediction of the effects of residue substitution on protein stability

The effects of residue substitution in protein can be dramatic and predicting its impact may benefit scientists greatly. Like in many scientific domains there are various methods and tools available to address the potential impact of a mutation on the structure of a protein. The identification of these methods, their availability, the time needed to gain enough familiarity with them and their interface, and the difficulty of integrating their results in a global view where all view points can be visualized often limit their use. In this paper, we present the Structural Prediction for pRotein fOlding UTility System (SPROUTS) workflow and describe our method for designing, documenting, and maintaining the workflow. The focus of the workflow is the thermodynamic contribution to stability, which can be considered as acceptable for small proteins. It compiles the predictions from various sources calculating the ΔΔG upon point mutation, together with a consensus from eight distinct algorithms, with a prediction of the mean number of interacting residues during the process of folding, and a sub domain structural analysis into fragments that may potentially be considered as autonomous folding units, i.e., with similar conformations alone and in the protein body. The workflow is implemented and available online. We illustrate its use with the analysis of the engrailed homeodomain (PDB code 1enh).

Correlating topology and thermodynamics to predict protein structure sensitivity to point mutations

Abstract The relation between distribution of hydrophobic amino acids along with protein chains and their structure is far from being completely understood. No reliable method allows ab initio prediction of the folded structure from this distribution of physicochemical properties, even when they are highly degenerated by considering only two classes: hydrophobic and polar. Establishment of long-range hydrophobic three dimension (3D) contacts is essential for the formation of the nucleus, a key process in the early steps of protein folding. Thus, a large number of 3D simulation studies were developed to challenge this issue. They are nowadays evaluated in a specific chapter of the molecular modeling competition, Critical Assessment of Protein Structure Prediction. We present here a simulation of the early steps of the folding process for 850 proteins, performed in a discrete 3D space, which results in peaks in the predicted distribution of intra-chain noncovalent contacts. The residues located at these peak positions tend to be buried in the core of the protein and are expected to correspond to critical positions in the sequence, important both for folding and structural (or similarly, energetic in the thermodynamic hypothesis) stability. The degree of stabilization or destabilization due to a point mutation at the critical positions involved in numerous contacts is estimated from the calculated folding free energy difference between mutated and native structures. The results show that these critical positions are not tolerant towards mutation. This simulation of the noncovalent contacts only needs a sequence as input, and this paper proposes a validation of the method by comparison with the prediction of stability by well-established programs.

Extracting Semantics from Legacy Scientific Workflows

In this paper we present a method that uses the Workflow Instrumentation for structure Extraction (WISE) combined with the SemanticMap methods to process ad-hoc legacy workflows written in Python and produce a mapping of the workflow structural skeleton to a domain ontology. The method provides the foundation for searching through scientific workflows with conceptual queries.

Chapter 24 – SMIR: A Web Server to Predict Residues Involved in the Protein Folding Core

Motivation: Protein folding is the critical spontaneous phase when the protein gains its structural conformation, and hence, its functional shape. Should any error in the process affect its folding, the protein structure may fail to fold properly and perform its function. In some cases, such misfolded proteins can cause disease. Mutations are typical causes of protein misfolding, but some residues are more likely than others to affect the folding process when mutated. This chapter presents a new method, called SMIR, that identifies the residues involved in the core of proteins, thus more sensitive to mutations. Results: A Monte Carlo algorithm is used to simulate the early steps of protein folding and the mean number of spatial, noncovalently bound neighbors is calculated after 106 steps. Residues surrounded by many others may play a role in the compactness of the protein and thus are called Most Interacting Residues (MIR). The original MIR method was updated and extended with a new smoothing method using hydrophobic-based residue neighborhood analysis. The resulting SMIR method is implemented and available as a server that supports the submission and the analysis of protein structures with MIR2.0 and SMIR. The server offers a dynamic interface with the display of results in a two-dimensional (2D) graph. Availability: SMIR is free and open to all users as a function of the Structural Prediction for pRotein fOlding UTility System (SPROUTS) with no login requirement at http://sprouts.rpbs.univ-paris-diderot.fr/mir.html. The new server also offers a user-friendly interface and unlimited access to results stored in a database. Supplementary information: The MIR method and SMIR extension are described in great details in the supplementary material available at Bioinformatics online.

Refurbishing Legacy Biological Workflows SPROUTS Case Study

Scientific discovery relies on an experimental framework that corroborates hypotheses with experiments that are complex reproducible processes generating and transforming large datasets. The methods, implicit in the process, capture the semantics of the data, thus they are responsible for the generation of scientific information and discovery of scientific knowledge. Scientific workflows provide the semantics needed to wrap scientific data from their capture, analysis, publication, and archival. By annotating data with the processes that produce them, the scientist no longer manages data but information and allows their meaningful interpretation and integration. Any change to a scientific workflow may impact significantly the quality of the data produced, their semantics, their future analysis, use, integration, and distribution, as well as the performance of the execution. Yet, scientific workflows are typically transformed over time, updated with new versions of the tools that compose them, extended to new functionality, and composed. In this paper we discuss the various impacts of workflow transformation and illustrate them with a case study on the Structural Prediction for pRotein fOlding UTility System (SPROUTS) Workflow.