Conformational buffering underlies functional selection in intrinsically disordered protein regions.

Many disordered proteins conserve essential functions in the face of extensive sequence variation, making it challenging to identify the mechanisms responsible for functional selection. Here we identify the molecular mechanism of functional selection for the disordered adenovirus early gene 1A (E1A) protein. E1A competes with host factors to bind the retinoblastoma (Rb) protein, subverting cell cycle regulation. We show that two binding motifs tethered by a hypervariable disordered linker drive picomolar affinity Rb binding and host factor displacement. Compensatory changes in amino acid sequence composition and sequence length lead to conservation of optimal tethering across a large family of E1A linkers. We refer to this compensatory mechanism as conformational buffering. We also detect coevolution of the motifs and linker, which can preserve or eliminate the tethering mechanism. Conformational buffering and motif-linker coevolution explain robust functional encoding within hypervariable disordered linkers and could underlie functional selection of many disordered protein regions.

Characterization of plant microRNA-encoded peptides (miPEPs) reveals molecular mechanisms from the translation to activity and specificity.

MicroRNAs (miRNAs) are transcribed as long primary transcripts (pri-miRNAs) by RNA polymerase II. Plant pri-miRNAs encode regulatory peptides called miPEPs, which specifically enhance the transcription of the pri-miRNA from which they originate. However, paradoxically, whereas miPEPs have been identified in different plant species, they are poorly conserved, raising the question of the mechanisms underlying their specificity. To address this point, we identify and re-annotate multiple Arabidopsis thaliana pri-miRNAs in order to identify ORF encoding miPEPs. The study of several identified miPEPs in different species show that non-conserved miPEPs are only active in their plant of origin, whereas conserved ones are active in different species. Finally, we find that miPEP activity relies on the presence of its own miORF, explaining both the lack of selection pressure on miPEP sequence and the ability for non-conserved peptides to play a similar role, i.e., to activate the expression of their corresponding miRNA.

MoMA-LoopSampler: a web server to exhaustively sample protein loop conformations.

SUMMARY: MoMA-LoopSampler is a sampling method that globally explores the conformational space of flexible protein loops. It combines a large structural library of three-residue fragments and a novel reinforcement-learning-based approach to accelerate the sampling process while maintaining diversity. The method generates a set of statistically likely loop states satisfying geometric constraints, and its ability to sample experimentally observed conformations has been demonstrated. This paper presents a web user interface to MoMA-LoopSampler through the illustration of a typical use-case. AVAILABILITY AND IMPLEMENTATION: MoMA-LoopSampler is freely available at: https://moma.laas.fr/applications/LoopSampler/. We recommend users to create an account, but anonymous access is possible. In most cases, jobs are completed within a few minutes. The waiting time may increase depending on the server load, but it very rarely exceeds an hour. For users requiring more intensive use, binaries can be provided upon request. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Current approaches to flexible loop modeling.

Loops are key components of protein structures, involved in many biological functions. Due to their conformational variability, the structural investigation of loops is a difficult topic, requiring a combination of experimental and computational methods. This paper provides a brief overview of current computational approaches to flexible loop modeling, and presents the main ingredients of the most standard protocols. Despite great progress in recent years, accurately modeling the conformational variability of long flexible loops remains a challenging problem. Future advances in this field will likely come from a tight coupling of experimental and computational techniques, which would enable a better understanding of the relationships between loop sequence, structural flexibility, and functional roles. In fine, accurate loop modeling will open the road to loop design problems of interest for applications in biomedicine and biotechnology.

Protein loops with multiple meta-stable conformations: A challenge for sampling and scoring methods.

Flexible regions in proteins, such as loops, cannot be represented by a single conformation. Instead, conformational ensembles are needed to provide a more global picture. In this context, identifying statistically meaningful conformations within an ensemble generated by loop sampling techniques remains an open problem. The difficulty is primarily related to the lack of structural data about these flexible regions. With the majority of structural data coming from x-ray crystallography and ignoring plasticity, the conception and evaluation of loop scoring methods is challenging. In this work, we compare the performance of various scoring methods on a set of eight protein loops that are known to be flexible. The ability of each method to identify and select all of the known conformations is assessed, and the underlying energy landscapes are produced and projected to visualize the qualitative differences obtained when using the methods. Statistical potentials are found to provide considerable reliability despite their being designed to tradeoff accuracy for lower computational cost. On a large pool of loop models, they are capable of filtering out statistically improbable states while retaining those that resemble known (and thus likely) conformations. However, computationally expensive methods are still required for more precise assessment and structural refinement. The results also highlight the importance of employing several scaffolds for the protein, due to the high influence of small structural rearrangements in the rest of the protein over the modeled energy landscape for the loop.

Predicting Secondary Structure Propensities in IDPs Using Simple Statistics from Three-Residue Fragments.

Intrinsically disordered proteins (IDPs) play key functional roles facilitated by their inherent plasticity. In most of the cases, IDPs recognize their partners through partially structured elements inserted in fully disordered chains. The identification and characterization of these elements is fundamental to understand the functional mechanisms of IDPs. Although several computational methods have been developed to identify order within disordered chains, most of the current secondary structure predictors are focused on globular proteins and are not necessarily appropriate for IDPs. Here, we present a comprehensible method, called Local Structural Propensity Predictor (LS2P), to predict secondary structure elements from IDP sequences. LS2P performs statistical analyses from a database of three-residue fragments extracted from coil regions of high-resolution protein structures. In addition to identifying scarcely populated helical and extended regions, the method pinpoints short stretches triggering ß-turn formation or promoting α-helices. The simplicity of the method enables a direct connection between experimental observations and structural features encoded in IDP sequences.

A reinforcement-learning-based approach to enhance exhaustive protein loop sampling.

MOTIVATION: Loop portions in proteins are involved in many molecular interaction processes. They often exhibit a high degree of flexibility, which can be essential for their function. However, molecular modeling approaches usually represent loops using a single conformation. Although this conformation may correspond to a (meta-)stable state, it does not always provide a realistic representation. RESULTS: In this paper, we propose a method to exhaustively sample the conformational space of protein loops. It exploits structural information encoded in a large library of three-residue fragments, and enforces loop-closure using a closed-form inverse kinematics solver. A novel reinforcement-learning-based approach is applied to accelerate sampling while preserving diversity. The performance of our method is showcased on benchmark datasets involving 9-, 12- and 15-residue loops. In addition, more detailed results presented for streptavidin illustrate the ability of the method to exhaustively sample the conformational space of loops presenting several meta-stable conformations. AVAILABILITY AND IMPLEMENTATION: We are developing a software package called MoMA (for Molecular Motion Algorithms), which includes modeling tools and algorithms to sample conformations and transition paths of biomolecules, including the application described in this work. The binaries can be provided upon request and a web application will also be implemented in the short future. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Conformational changes in antibody Fab fragments upon binding and their consequences on the performance of docking algorithms.

BACKGROUND: The existence of conformational changes in antibodies upon binding has been previously established. However, existing analyses focus on individual cases and no quantitative study provides a more global view of potential moves and repacking, especially on recent data. The present study focuses on analyzing the conformational changes in various antibodies upon binding, providing quantitative observations to be exploited for antibody-related modeling. METHODS: Cartesian and dihedral Root-Mean-Squared Deviations were calculated for different subparts of 27 different antibodies, for which X-ray structures in the bound and unbound states are available. Elbow angle variations were also calculated. Previously reported results of four docking algorithms were condensed into one score giving overall docking success for each of 16 antibody-antigen cases. RESULTS: Very diverse movements are observed upon binding. While many loops stay very rigid, several others display side-chain repacking or backbone rearrangements, or both, at many different levels. Large conformational changes restricted to one or more antibody hypervariable loops were found to be a better indicator of docking difficulty than overall conformational variation at the antibody-antigen interface. However, the failure of docking algorithms on some almost-rigid cases shows that scoring is still a major bottleneck in docking pose prediction. CONCLUSIONS: This study is aimed to help scientists working on antibody analysis and design by giving insights into the nature and the extent of conformational changes at different levels upon antigen binding.

Utility of Resazurin, Horseradish Peroxidase, and NMR Assays to Identify Redox-Related False-Positive Behavior in High-Throughput Screens.

Discerning false positives from true actives in high-throughput screening (HTS) output is fraught with difficulty as the reason of anomalous activity seen for compounds is often not clear-cut. In this study, we introduce a novel medium-throughput NMR assay for the identification of redox-cycling compounds (RCCs), which is based on detection of oxidation of a reducing agent. We compare its outcomes to those from horseradish peroxidase (HRP)/phenol red and resazurin (RZ)-based assays that are more commonly used for triaging HTS outputs. Data from NMR, RZ, and HRP redox assay are shown to correlate, with the NMR assay showing the greatest accuracy. In addition, historical data analysis was used to identify compounds frequently active in assays for redox-susceptible targets. We provide examples of compound classes found and conclude that the NMR redox assay offers a novel and reliable way of identifying RCCs at a medium throughput. The HRP and RZ assays are reasonable higher-throughput alternatives, with both showing similar sensitivity to redox-cycling and false-positive compounds. The RZ assay has a higher hit rate, reflecting its ability to pick up multiple modes of action.

CytoASP: a Cytoscape app for qualitative consistency reasoning, prediction and repair in biological networks.

BACKGROUND: Qualitative reasoning frameworks, such as the Sign Consistency Model (SCM), enable modelling regulatory networks to check whether observed behaviour can be explained or if unobserved behaviour can be predicted. The BioASP software collection offers ideal tools for such analyses. Additionally, the Cytoscape platform can offer extensive functionality and visualisation capabilities. However, specialist programming knowledge is required to use BioASP and no methods exist to integrate both of these software platforms effectively. RESULTS: We report the implementation of CytoASP, an app that allows the use of BioASP for influence graph consistency checking, prediction and repair operations through Cytoscape. While offering inherent benefits over traditional approaches using BioASP, it provides additional advantages such as customised visualisation of predictions and repairs, as well as the ability to analyse multiple networks in parallel, exploiting multi-core architecture. We demonstrate its usage in a case study of a yeast genetic network, and highlight its capabilities in reasoning over regulatory networks. CONCLUSION: We have presented a user-friendly Cytoscape app for the analysis of regulatory networks using BioASP. It allows easy integration of qualitative modelling, combining the functionality of BioASP with the visualisation and processing capability in Cytoscape, and thereby greatly simplifying qualitative network modelling, promoting its use in relevant projects.