Substrate recognition principles for the PP2A-B55 protein phosphatase.

The PP2A-B55 phosphatase regulates a plethora of signaling pathways throughout eukaryotes. How PP2A-B55 selects its substrates presents a severe knowledge gap. By integrating AlphaFold modeling with comprehensive high-resolution mutational scanning, we show that α helices in substrates bind B55 through an evolutionary conserved mechanism. Despite a large diversity in sequence and composition, these α helices share key amino acid determinants that engage discrete hydrophobic and electrostatic patches. Using deep learning protein design, we generate a specific and potent competitive peptide inhibitor of PP2A-B55 substrate interactions. With this inhibitor, we uncover that PP2A-B55 regulates the nuclear exosome targeting (NEXT) complex by binding to an α-helical recruitment module in the RNA binding protein 7 (RBM7), a component of the NEXT complex. Collectively, our findings provide a framework for the understanding and interrogation of PP2A-B55 function in health and disease.

Systematic discovery of protein interaction interfaces using AlphaFold and experimental validation.

Structural resolution of protein interactions enables mechanistic and functional studies as well as interpretation of disease variants. However, structural data is still missing for most protein interactions because we lack computational and experimental tools at scale. This is particularly true for interactions mediated by short linear motifs occurring in disordered regions of proteins. We find that AlphaFold-Multimer predicts with high sensitivity but limited specificity structures of domain-motif interactions when using small protein fragments as input. Sensitivity decreased substantially when using long protein fragments or full length proteins. We delineated a protein fragmentation strategy particularly suited for the prediction of domain-motif interfaces and applied it to interactions between human proteins associated with neurodevelopmental disorders. This enabled the prediction of highly confident and likely disease-related novel interfaces, which we further experimentally corroborated for FBXO23-STX1B, STX1B-VAMP2, ESRRG-PSMC5, PEX3-PEX19, PEX3-PEX16, and SNRPB-GIGYF1 providing novel molecular insights for diverse biological processes. Our work highlights exciting perspectives, but also reveals clear limitations and the need for future developments to maximize the power of Alphafold-Multimer for interface predictions.

Structural study of UFL1-UFC1 interaction uncovers the role of UFL1 N-terminal helix in ufmylation.

Ufmylation plays a crucial role in various cellular processes including DNA damage response, protein translation, and ER homeostasis. To date, little is known about how the enzymes responsible for ufmylation coordinate their action. Here, we study the details of UFL1 (E3) activity, its binding to UFC1 (E2), and its relation to UBA5 (E1), using a combination of structural modeling, X-ray crystallography, NMR, and biochemical assays. Guided by Alphafold2 models, we generate an active UFL1 fusion construct that includes its partner DDRGK1 and solve the crystal structure of this critical interaction. This fusion construct also unveiled the importance of the UFL1 N-terminal helix for binding to UFC1. The binding site suggested by our UFL1-UFC1 model reveals a conserved interface, and competition between UFL1 and UBA5 for binding to UFC1. This competition changes in the favor of UFL1 following UFM1 charging of UFC1. Altogether, our study reveals a novel, terminal helix-mediated regulatory mechanism, which coordinates the cascade of E1-E2-E3-mediated transfer of UFM1 to its substrate and provides new leads to target this modification.

Discriminating physiological from non-physiological interfaces in structures of protein complexes: A community-wide study.

Reliably scoring and ranking candidate models of protein complexes and assigning their oligomeric state from the structure of the crystal lattice represent outstanding challenges. A community-wide effort was launched to tackle these challenges. The latest resources on protein complexes and interfaces were exploited to derive a benchmark dataset consisting of 1677 homodimer protein crystal structures, including a balanced mix of physiological and non-physiological complexes. The non-physiological complexes in the benchmark were selected to bury a similar or larger interface area than their physiological counterparts, making it more difficult for scoring functions to differentiate between them. Next, 252 functions for scoring protein-protein interfaces previously developed by 13 groups were collected and evaluated for their ability to discriminate between physiological and non-physiological complexes. A simple consensus score generated using the best performing score of each of the 13 groups, and a cross-validated Random Forest (RF) classifier were created. Both approaches showed excellent performance, with an area under the Receiver Operating Characteristic (ROC) curve of 0.93 and 0.94, respectively, outperforming individual scores developed by different groups. Additionally, AlphaFold2 engines recalled the physiological dimers with significantly higher accuracy than the non-physiological set, lending support to the reliability of our benchmark dataset annotations. Optimizing the combined power of interface scoring functions and evaluating it on challenging benchmark datasets appears to be a promising strategy.

Who Binds Better? Let Alphafold2 Decide!

Deep learning is revolutionizing structural biology to an unprecedented extent. Spearheaded by DeepMind's Alphafold2, structural models of high quality can be generated, and are now available for most known proteins and many protein interactions. The next challenge will be to leverage this rich structural corpus to learn about binding: which protein can contact which partner(s), and at what affinity? In a recent study, Chang and Perez have presented an elegant approach towards this challenging goal for interactions that involve a short peptide binding to its receptor. The basic idea is straightforward: given a receptor that binds to two peptides, if the receptor sequence is presented with both peptides together at the same time, AlphaFold2 should model the tighter binding peptide into the binding site, while excluding the second. A simple idea that works!

Matching protein surface structural patches for high-resolution blind peptide docking.

Peptide docking can be perceived as a subproblem of protein­protein docking. However, due to the short length and flexible nature of peptides, many do not adopt one defined conformation prior to binding. Therefore, to tackle a peptide docking problem, not only the relative orientation, but also the bound conformation of the peptide needs to be modeled. Traditional peptide-centered approaches use information about peptide sequences to generate representative conformer ensembles, which can then be rigid-body docked to the receptor. Alternatively, one may look at this problem from the viewpoint of the receptor, namely, that the protein surface defines the peptide-bound conformation. Here, we present PatchMAN (Patch-Motif AligNments), a global peptide-docking approach that uses structural motifs to map the receptor surface with backbone scaffolds extracted from protein structures. On a nonredundant set of protein­peptide complexes, starting from free receptor structures, PatchMAN successfully models and identifies near-native peptide­protein complexes in 58%/84% within 2.5 Å/5 Å interface backbone RMSD, with corresponding sampling in 81%/100% of the cases, outperforming other approaches. PatchMAN leverages the observation that structural units of peptides with their binding pocket can be found not only within interfaces, but also within monomers. We show that the bound peptide conformation is sampled based on the structural context of the receptor only, without taking into account any sequence information. Beyond peptide docking, this approach opens exciting new avenues to study principles of peptide­protein association, and to the design of new peptide binders. PatchMAN is available as a server at https://furmanlab.cs.huji.ac.il/patchman/.

Structure-based prediction of HDAC6 substrates validated by enzymatic assay reveals determinants of promiscuity and detects new potential substrates.

Histone deacetylases play important biological roles well beyond the deacetylation of histone tails. In particular, HDAC6 is involved in multiple cellular processes such as apoptosis, cytoskeleton reorganization, and protein folding, affecting substrates such as ɑ-tubulin, Hsp90 and cortactin proteins. We have applied a biochemical enzymatic assay to measure the activity of HDAC6 on a set of candidate unlabeled peptides. These served for the calibration of a structure-based substrate prediction protocol, Rosetta FlexPepBind, previously used for the successful substrate prediction of HDAC8 and other enzymes. A proteome-wide screen of reported acetylation sites using our calibrated protocol together with the enzymatic assay provide new peptide substrates and avenues to novel potential functional regulatory roles of this promiscuous, multi-faceted enzyme. In particular, we propose novel regulatory roles of HDAC6 in tumorigenesis and cancer cell survival via the regulation of EGFR/Akt pathway activation. The calibration process and comparison of the results between HDAC6 and HDAC8 highlight structural differences that explain the established promiscuity of HDAC6.

Harnessing protein folding neural networks for peptide-protein docking.

Highly accurate protein structure predictions by deep neural networks such as AlphaFold2 and RoseTTAFold have tremendous impact on structural biology and beyond. Here, we show that, although these deep learning approaches have originally been developed for the in silico folding of protein monomers, AlphaFold2 also enables quick and accurate modeling of peptide-protein interactions. Our simple implementation of AlphaFold2 generates peptide-protein complex models without requiring multiple sequence alignment information for the peptide partner, and can handle binding-induced conformational changes of the receptor. We explore what AlphaFold2 has memorized and learned, and describe specific examples that highlight differences compared to state-of-the-art peptide docking protocol PIPER-FlexPepDock. These results show that AlphaFold2 holds great promise for providing structural insight into a wide range of peptide-protein complexes, serving as a starting point for the detailed characterization and manipulation of these interactions.

A method for characterizing Cas9 variants via a one-million target sequence library of self-targeting sgRNAs.

Detailed target-selectivity information and experiment-based efficacy prediction tools are primarily available for Streptococcus pyogenes Cas9 (SpCas9). One obstacle to develop such tools is the rarity of accurate data. Here, we report a method termed 'Self-targeting sgRNA Library Screen' (SLS) for assaying the activity of Cas9 nucleases in bacteria using random target/sgRNA libraries of self-targeting sgRNAs. Exploiting more than a million different sequences, we demonstrate the use of the method with the SpCas9-HF1 variant to analyse its activity and reveal motifs that influence its target-selectivity. We have also developed an algorithm for predicting the activity of SpCas9-HF1 with an accuracy matching those of existing tools. SLS is a facile alternative to the much more expensive and laborious approaches used currently and has the capability of delivering sufficient amount of data for most of the orthologs and variants of SpCas9.

The TMCrys server for supporting crystallization of transmembrane proteins.

MOTIVATION: Due to their special properties, the structures of transmembrane proteins are extremely hard to determine. Several methods exist to predict the propensity of successful completion of the structure determination process. However, available predictors incorporate data of any kind of proteins, hence they can hardly differentiate between crystallizable and non-crystallizable membrane proteins. RESULTS: We implemented a web server to simplify running TMCrys prediction method that was developed specifically to separate crystallizable and non-crystallizable membrane proteins. AVAILABILITY AND IMPLEMENTATION: http://tmcrys.enzim.ttk.mta.hu. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

TMCrys: predict propensity of success for transmembrane protein crystallization.

Motivation: Transmembrane proteins (TMPs) are crucial in the life of the cells. As they have special properties, their structure is hard to determine--the PDB database consists of 2% TMPs, despite the fact that they are predicted to make up to 25% of the human proteome. Crystallization prediction methods were developed to aid the target selection for structure determination, however, there is a need for a TMP specific service. Results: Here, we present TMCrys, a crystallization prediction method that surpasses existing prediction methods in performance thanks to its specialization for TMPs. We expect TMCrys to improve target selection of TMPs. Availability and implementation: https://github.com/brgenzim/tmcrys. Supplementary information: Supplementary data are available at Bioinformatics online.