Local Disordered Region Sampling (LDRS) for ensemble modeling of proteins with experimentally undetermined or low confidence prediction segments.

SUMMARY: The Local Disordered Region Sampling (LDRS, pronounced loaders) tool is a new module developed for IDPConformerGenerator, a previously validated approach to model intrinsically disordered proteins (IDPs). The IDPConformerGenerator LDRS module provides a method for generating all-atom conformations of intrinsically disordered protein regions at N- and C-termini of and in loops or linkers between folded regions of an existing protein structure. These disordered elements often lead to missing coordinates in experimental structures or low confidence in predicted structures. Requiring only a pre-existing PDB or mmCIF formatted structural template of the protein with missing coordinates or with predicted confidence scores and its full-length primary sequence, LDRS will automatically generate physically meaningful conformational ensembles of the missing flexible regions to complete the full-length protein. The capabilities of the LDRS tool of IDPConformerGenerator include modeling phosphorylation sites using enhanced Monte Carlo-Side Chain Entropy, transmembrane proteins within an all-atom bilayer, and multi-chain complexes. The modeling capacity of LDRS capitalizes on the modularity, the ability to be used as a library and via command-line, and the computational speed of the IDPConformerGenerator platform. AVAILABILITY AND IMPLEMENTATION: The LDRS module is part of the IDPConformerGenerator modeling suite, which can be downloaded from GitHub at https://github.com/julie-forman-kay-lab/IDPConformerGenerator. IDPConformerGenerator is written in Python3 and works on Linux, Microsoft Windows, and Mac OS versions that support DSSP. Users can utilize LDRS's Python API for scripting the same way they can use any part of IDPConformerGenerator's API, by importing functions from the "idpconfgen.ldrs_helper" library. Otherwise, LDRS can be used as a command line interface application within IDPConformerGenerator. Full documentation is available within the command-line interface as well as on IDPConformerGenerator's official documentation pages (https://idpconformergenerator.readthedocs.io/en/latest/).

Impact of AlphaFold on structure prediction of protein complexes: The CASP15-CAPRI experiment.

We present the results for CAPRI Round 54, the 5th joint CASP-CAPRI protein assembly prediction challenge. The Round offered 37 targets, including 14 homodimers, 3 homo-trimers, 13 heterodimers including 3 antibody-antigen complexes, and 7 large assemblies. On average ~70 CASP and CAPRI predictor groups, including more than 20 automatics servers, submitted models for each target. A total of 21 941 models submitted by these groups and by 15 CAPRI scorer groups were evaluated using the CAPRI model quality measures and the DockQ score consolidating these measures. The prediction performance was quantified by a weighted score based on the number of models of acceptable quality or higher submitted by each group among their five best models. Results show substantial progress achieved across a significant fraction of the 60+ participating groups. High-quality models were produced for about 40% of the targets compared to 8% two years earlier. This remarkable improvement is due to the wide use of the AlphaFold2 and AlphaFold2-Multimer software and the confidence metrics they provide. Notably, expanded sampling of candidate solutions by manipulating these deep learning inference engines, enriching multiple sequence alignments, or integration of advanced modeling tools, enabled top performing groups to exceed the performance of a standard AlphaFold2-Multimer version used as a yard stick. This notwithstanding, performance remained poor for complexes with antibodies and nanobodies, where evolutionary relationships between the binding partners are lacking, and for complexes featuring conformational flexibility, clearly indicating that the prediction of protein complexes remains a challenging problem.

Local Disordered Region Sampling (LDRS) for Ensemble Modeling of Proteins with Experimentally Undetermined or Low Confidence Prediction Segments.

The Local Disordered Region Sampling (LDRS, pronounced loaders) tool, developed for the IDPConformerGenerator platform (Teixeira et al. 2022), provides a method for generating all-atom conformations of intrinsically disordered regions (IDRs) at N- and C-termini of and in loops or linkers between folded regions of an existing protein structure. These disordered elements often lead to missing coordinates in experimental structures or low confidence in predicted structures. Requiring only a pre-existing PDB structure of the protein with missing coordinates or with predicted confidence scores and its full-length primary sequence, LDRS will automatically generate physically meaningful conformational ensembles of the missing flexible regions to complete the full-length protein. The capabilities of the LDRS tool of IDPConformerGenerator include modeling phosphorylation sites using enhanced Monte Carlo Side Chain Entropy (MC-SCE) (Bhowmick and Head-Gordon 2015), transmembrane proteins within an all-atom bilayer, and multi-chain complexes. The modeling capacity of LDRS capitalizes on the modularity, ability to be used as a library and via command-line, and computational speed of the IDPConformerGenerator platform.

Learning to evolve structural ensembles of unfolded and disordered proteins using experimental solution data.

The structural characterization of proteins with a disorder requires a computational approach backed by experiments to model their diverse and dynamic structural ensembles. The selection of conformational ensembles consistent with solution experiments of disordered proteins highly depends on the initial pool of conformers, with currently available tools limited by conformational sampling. We have developed a Generative Recurrent Neural Network (GRNN) that uses supervised learning to bias the probability distributions of torsions to take advantage of experimental data types such as nuclear magnetic resonance J-couplings, nuclear Overhauser effects, and paramagnetic resonance enhancements. We show that updating the generative model parameters according to the reward feedback on the basis of the agreement between experimental data and probabilistic selection of torsions from learned distributions provides an alternative to existing approaches that simply reweight conformers of a static structural pool for disordered proteins. Instead, the biased GRNN, DynamICE, learns to physically change the conformations of the underlying pool of the disordered protein to those that better agree with experiments.

FuzPred: a web server for the sequence-based prediction of the context-dependent binding modes of proteins.

Proteins form complex interactions in the cellular environment to carry out their functions. They exhibit a wide range of binding modes depending on the cellular conditions, which result in a variety of ordered or disordered assemblies. To help rationalise the binding behavior of proteins, the FuzPred server predicts their sequence-based binding modes without specifying their binding partners. The binding mode defines whether the bound state is formed through a disorder-to-order transition resulting in a well-defined conformation, or through a disorder-to-disorder transition where the binding partners remain conformationally heterogeneous. To account for the context-dependent nature of the binding modes, the FuzPred method also estimates the multiplicity of binding modes, the likelihood of sampling multiple binding modes. Protein regions with a high multiplicity of binding modes may serve as regulatory sites or hot-spots for structural transitions in the assembly. To facilitate the interpretation of the predictions, protein regions with different interaction behaviors can be visualised on protein structures generated by AlphaFold. The FuzPred web server (https://fuzpred.bio.unipd.it) thus offers insights into the structural and dynamical changes of proteins upon interactions and contributes to development of structure-function relationships under a variety of cellular conditions.

Alternatively spliced exon regulates context-dependent MEF2D higher-order assembly during myogenesis.

During muscle cell differentiation, the alternatively spliced, acidic ß-domain potentiates transcription of Myocyte-specific Enhancer Factor 2 (Mef2D). Sequence analysis by the FuzDrop method indicates that the ß-domain can serve as an interaction element for Mef2D higher-order assembly. In accord, we observed Mef2D mobile nuclear condensates in C2C12 cells, similar to those formed through liquid-liquid phase separation. In addition, we found Mef2D solid-like aggregates in the cytosol, the presence of which correlated with higher transcriptional activity. In parallel, we observed a progress in the early phase of myotube development, and higher MyoD and desmin expression. In accord with our predictions, the formation of aggregates was promoted by rigid ß-domain variants, as well as by a disordered ß-domain variant, capable of switching between liquid-like and solid-like higher-order states. Along these lines, NMR and molecular dynamics simulations corroborated that the ß-domain can sample both ordered and disordered interactions leading to compact and extended conformations. These results suggest that ß-domain fine-tunes Mef2D higher-order assembly to the cellular context, which provides a platform for myogenic regulatory factors and the transcriptional apparatus during the developmental process.

Learning Correlations between Internal Coordinates to Improve 3D Cartesian Coordinates for Proteins.

We consider a generic representation problem of internal coordinates (bond lengths, valence angles, and dihedral angles) and their transformation to 3-dimensional Cartesian coordinates of a biomolecule. We show that the internal-to-Cartesian process relies on correctly predicting chemically subtle correlations among the internal coordinates themselves, and learning these correlations increases the fidelity of the Cartesian representation. We developed a machine learning algorithm, Int2Cart, to predict bond lengths and bond angles from backbone torsion angles and residue types of a protein, which allows reconstruction of protein structures better than using fixed bond lengths and bond angles or a static library method that relies on backbone torsion angles and residue types in a local environment. The method is able to be used for structure validation, as we show that the agreement between Int2Cart-predicted bond geometries and those from an AlphaFold 2 model can be used to estimate model quality. Additionally, by using Int2Cart to reconstruct an IDP ensemble, we are able to decrease the clash rate during modeling. The Int2Cart algorithm has been implemented as a publicly accessible python package at https://github.com/THGLab/int2cart.

Protein interactions: anything new?

How do proteins interact in the cellular environment? Which interactions stabilize liquid-liquid phase separated condensates? Are the concepts, which have been developed for specific protein complexes also applicable to higher-order assemblies? Recent discoveries prompt for a universal framework for protein interactions, which can be applied across the scales of protein communities. Here, we discuss how our views on protein interactions have evolved from rigid structures to conformational ensembles of proteins and discuss the open problems, in particular related to biomolecular condensates. Protein interactions have evolved to follow changes in the cellular environment, which manifests in multiple modes of interactions between the same partners. Such cellular context-dependence requires multiplicity of binding modes (MBM) by sampling multiple minima of the interaction energy landscape. We demonstrate that the energy landscape framework of protein folding can be applied to explain this phenomenon, opening a perspective toward a physics-based, universal model for cellular protein behaviors.

IDPConformerGenerator: A Flexible Software Suite for Sampling the Conformational Space of Disordered Protein States.

The power of structural information for informing biological mechanisms is clear for stable folded macromolecules, but similar structure-function insight is more difficult to obtain for highly dynamic systems such as intrinsically disordered proteins (IDPs) which must be described as structural ensembles. Here, we present IDPConformerGenerator, a flexible, modular open-source software platform for generating large and diverse ensembles of disordered protein states that builds conformers that obey geometric, steric, and other physical restraints on the input sequence. IDPConformerGenerator samples backbone phi (φ), psi (ψ), and omega (ω) torsion angles of relevant sequence fragments from loops and secondary structure elements extracted from folded protein structures in the RCSB Protein Data Bank and builds side chains from robust Monte Carlo algorithms using expanded rotamer libraries. IDPConformerGenerator has many user-defined options enabling variable fractional sampling of secondary structures, supports Bayesian models for assessing the agreement of IDP ensembles for consistency with experimental data, and introduces a machine learning approach to transform between internal and Cartesian coordinates with reduced error. IDPConformerGenerator will facilitate the characterization of disordered proteins to ultimately provide structural insights into these states that have key biological functions.

Protein Dynamics to Define and Refine Disordered Protein Ensembles.

Intrinsically disordered proteins and unfolded proteins have fluctuating conformational ensembles that are fundamental to their biological function and impact protein folding, stability, and misfolding. Despite the importance of protein dynamics and conformational sampling, time-dependent data types are not fully exploited when defining and refining disordered protein ensembles. Here we introduce a computational framework using an elastic network model and normal-mode displacements to generate a dynamic disordered ensemble consistent with NMR-derived dynamics parameters, including transverse R2 relaxation rates and Lipari-Szabo order parameters (S2 values). We illustrate our approach using the unfolded state of the drkN SH3 domain to show that the dynamical ensembles give better agreement than a static ensemble for a wide range of experimental validation data including NMR chemical shifts, J-couplings, nuclear Overhauser effects, paramagnetic relaxation enhancements, residual dipolar couplings, hydrodynamic radii, single-molecule fluorescence Förster resonance energy transfer, and small-angle X-ray scattering.

A Methionine Chemical Shift Based Order Parameter Characterizing Global Protein Dynamics.

Coupling of side chain dynamics over long distances is an important component of allostery. Methionine side chains show the largest intrinsic flexibility among methyl-containing residues but the actual degree of conformational averaging depends on the proximity and mobility of neighboring residues. The 13 C NMR chemical shifts of the methyl groups of methionine residues located at long distances in the same protein show a similar scaling with respect to the values predicted from the static X-ray structure by quantum methods. This results in a good linear correlation between calculated and observed chemical shifts. The slope is protein dependent and ranges from zero for the highly flexible calmodulin to 0.7 for the much more rigid calcineurin catalytic domain. The linear correlation is indicative of a similar level of side-chain conformational averaging over long distances, and the slope of the correlation line can be interpreted as an order parameter of the global side-chain flexibility.

PDB-tools web: A user-friendly interface for the manipulation of PDB files.

The Protein Data Bank (PDB) file format remains a popular format used and supported by many software to represent coordinates of macromolecular structures. It however suffers from drawbacks such as error-prone manual editing. Because of that, various software toolkits have been developed to facilitate its editing and manipulation, but, to date, there is no online tool available for this purpose. Here we present PDB-Tools Web, a flexible online service for manipulating PDB files. It offers a rich and user-friendly graphical user interface that allows users to mix-and-match more than 40 individual tools from the pdb-tools suite. Those can be combined in a few clicks to perform complex pipelines, which can be saved and uploaded. The resulting processed PDB files can be visualized online and downloaded. The web server is freely available at https://wenmr.science.uu.nl/pdbtools.

Extended Experimental Inferential Structure Determination Method in Determining the Structural Ensembles of Disordered Protein States.

Proteins with intrinsic or unfolded state disorder comprise a new frontier in structural biology, requiring the characterization of diverse and dynamic structural ensembles. We introduce a comprehensive Bayesian framework, the Extended Experimental Inferential Structure Determination (X-EISD) method, that calculates the maximum log-likelihood of a disordered protein ensemble. X-EISD accounts for the uncertainties of a range of experimental data and back-calculation models from structures, including NMR chemical shifts, J-couplings, Nuclear Overhauser Effects (NOEs), paramagnetic relaxation enhancements (PREs), residual dipolar couplings (RDCs), hydrodynamic radii (R h ), single molecule fluorescence Förster resonance energy transfer (smFRET) and small angle X-ray scattering (SAXS). We apply X-EISD to the joint optimization against experimental data for the unfolded drkN SH3 domain and find that combining a local data type, such as chemical shifts or J-couplings, paired with long-ranged restraints such as NOEs, PREs or smFRET, yields structural ensembles in good agreement with all other data types if combined with representative IDP conformers.

A Myristoyl-Binding Site in the SH3 Domain Modulates c-Src Membrane Anchoring.

The c-Src oncogene is anchored to the cytoplasmic membrane through its N-terminal myristoylated SH4 domain. This domain is part of an intramolecular fuzzy complex with the SH3 and Unique domains. Here we show that the N-terminal myristoyl group binds to the SH3 domain in the proximity of the RT loop, when Src is not anchored to a lipid membrane. Residues in the so-called Unique Lipid Binding Region modulate this interaction. In the presence of lipids, the myristoyl group is released from the SH3 domain and inserts into the lipid membrane. The fuzzy complex with the SH4 and Unique domains is retained in the membrane-bound form, placing the SH3 domain close to the membrane surface and restricting its orientation. The apparent affinity of myristoylated proteins containing the SH4, Unique, and SH3 domains is modulated by these intramolecular interactions, suggesting a mechanism linking c-Src activation and membrane anchoring.

Cis-trans proline isomers in the catalytic domain of calcineurin.

Calcineurin is an essential calcium-activated serine/threonine phosphatase. The six NMR-observable methionine methyl groups in the catalytic domain of human calcineurin Aα (CNA) were assigned and used as reporters of the presence of potential cis-trans isomers in solution. Proline 84 is found in the cis conformation in most calcineurin X-ray structures, and proline 309, which is part of a highly conserved motif in phosphoprotein phosphatases, was modeled with a cis peptide bond in one of the two molecules present in the asymmetric unit of CNA. We mutated each of the two prolines to alanine to force the trans conformation. Solution NMR shows that the P84A CNA mutant exists in two forms, compatible with cis-trans isomers, while the P309A mutant is predominantly in the trans conformation. DATABASE: PDB depositions mentioned PDB 5C1V and 2JOG.

The Two Isoforms of Lyn Display Different Intramolecular Fuzzy Complexes with the SH3 Domain.

The function of the intrinsically disordered Unique domain of the Src family of tyrosine kinases (SFK), where the largest differences between family members are concentrated, remains poorly understood. Recent studies in c-Src have demonstrated that the Unique region forms transient interactions, described as an intramolecular fuzzy complex, with the SH3 domain and suggested that similar complexes could be formed by other SFKs. Src and Lyn are members of a distinct subfamily of SFKs. Lyn is a key player in the immunologic response and exists in two isoforms originating from alternative splicing in the Unique domain. We have used NMR to compare the intramolecular interactions in the two isoforms and found that the alternatively spliced segment interacts specifically with the so-called RT-loop in the SH3 domain and that this interaction is abolished when a polyproline ligand binds to the SH3 domain. These results support the generality of the fuzzy complex formation in distinct subfamilies of SFKs and its physiological role, as the naturally occurring alternative splicing modulates the interactions in this complex.

Intramolecular Fuzzy Interactions Involving Intrinsically Disordered Domains.

Structural disorder is an essential ingredient for function in many proteins and protein complexes. Fuzzy complexes describe the many instances where disorder is maintained as a critical element of protein interactions. In this minireview we discuss how intramolecular fuzzy interactions function in signaling complexes. Focussing on the Src family of kinases, we argue that the intrinsically disordered domains that are unique for each of the family members and display a clear fingerprint of long range interactions in Src, might have critical roles as functional sensor or effectors and mediate allosteric communication via fuzzy interactions.

Farseer-NMR: automatic treatment, analysis and plotting of large, multi-variable NMR data.

We present Farseer-NMR ( https://git.io/vAueU ), a software package to treat, evaluate and combine NMR spectroscopic data from sets of protein-derived peaklists covering a range of experimental conditions. The combined advances in NMR and molecular biology enable the study of complex biomolecular systems such as flexible proteins or large multibody complexes, which display a strong and functionally relevant response to their environmental conditions, e.g. the presence of ligands, site-directed mutations, post translational modifications, molecular crowders or the chemical composition of the solution. These advances have created a growing need to analyse those systems' responses to multiple variables. The combined analysis of NMR peaklists from large and multivariable datasets has become a new bottleneck in the NMR analysis pipeline, whereby information-rich NMR-derived parameters have to be manually generated, which can be tedious, repetitive and prone to human error, or even unfeasible for very large datasets. There is a persistent gap in the development and distribution of software focused on peaklist treatment, analysis and representation, and specifically able to handle large multivariable datasets, which are becoming more commonplace. In this regard, Farseer-NMR aims to close this longstanding gap in the automated NMR user pipeline and, altogether, reduce the time burden of analysis of large sets of peaklists from days/weeks to seconds/minutes. We have implemented some of the most common, as well as new, routines for calculation of NMR parameters and several publication-quality plotting templates to improve NMR data representation. Farseer-NMR has been written entirely in Python and its modular code base enables facile extension.

pdb-tools: a swiss army knife for molecular structures.

The pdb-tools are a collection of Python scripts for working with molecular structure data in the Protein Data Bank (PDB) format. They allow users to edit, convert, and validate PDB files, from the command-line, in a simple but efficient manner. The pdb-tools are implemented in Python, without any external dependencies, and are freely available under the open-source Apache License at https://github.com/haddocking/pdb-tools/ and on PyPI.