AI approaches for the discovery and validation of drug targets.

Artificial intelligence (AI) holds immense promise for accelerating and improving all aspects of drug discovery, not least target discovery and validation. By integrating a diverse range of biological data modalities, AI enables the accurate prediction of drug target properties, ultimately illuminating biological mechanisms of disease and guiding drug discovery strategies. Despite the indisputable potential of AI in drug target discovery, there are many challenges and obstacles yet to be overcome, including dealing with data biases, model interpretability and generalisability, and the validation of predicted drug targets, to name a few. By exploring recent advancements in AI, this review showcases current applications of AI for drug target discovery and offers perspectives on the future of AI for the discovery and validation of drug targets, paving the way for the generation of novel and safer pharmaceuticals.

Proline-rich regions in transcriptional complexes: heading in many directions.

Although many protein complexes are now known, the precise details of how each component operates are rarely understood. Through a combination of bioinformatics and analysis of gene-trapped mouse clones, Enkhmandakh et al. were able to deduce the modular function for a part of Ssdp1, a crucial component of the Ldb1 transcriptional complex, which plays a central role in mammalian head development. A proline-rich module from Ssdp1 is likely responsible for transactivation, and this region is curiously mobile, occurring in different proteins in different species. The results underscore the importance of protein modularity in complex organization, as well as the role of irregular or disordered parts of proteins in crucial biological processes.

Systematic discovery of new recognition peptides mediating protein interaction networks.

Many aspects of cell signalling, trafficking, and targeting are governed by interactions between globular protein domains and short peptide segments. These domains often bind multiple peptides that share a common sequence pattern, or "linear motif" (e.g., SH3 binding to PxxP). Many domains are known, though comparatively few linear motifs have been discovered. Their short length (three to eight residues), and the fact that they often reside in disordered regions in proteins makes them difficult to detect through sequence comparison or experiment. Nevertheless, each new motif provides critical molecular details of how interaction networks are constructed, and can explain how one protein is able to bind to very different partners. Here we show that binding motifs can be detected using data from genome-scale interaction studies, and thus avoid the normally slow discovery process. Our approach based on motif over-representation in non-homologous sequences, rediscovers known motifs and predicts dozens of others. Direct binding experiments reveal that two predicted motifs are indeed protein-binding modules: a DxxDxxxD protein phosphatase 1 binding motif with a KD of 22 microM and a VxxxRxYS motif that binds Translin with a KD of 43 microM. We estimate that there are dozens or even hundreds of linear motifs yet to be discovered that will give molecular insight into protein networks and greatly illuminate cellular processes.

DILIMOT: discovery of linear motifs in proteins.

Discovery of protein functional motifs is critical in modern biology. Small segments of 3-10 residues play critical roles in protein interactions, post-translational modifications and trafficking. DILIMOT (DIscovery of LInear MOTifs) is a server for the prediction of these short linear motifs within a set of proteins. Given a set of sequences sharing a common functional feature (e.g. interaction partner or localization) the method finds statistically over-represented motifs likely to be responsible for it. The input sequences are first passed through a set of filters to remove regions unlikely to contain instances of linear motifs. Motifs are then found in the remaining sequence and ranked according to a statistic that measure over-representation and conservation across homologues in related species. The results are displayed via a visual interface for easy perusal. The server is available at http://dilimot.embl.de.

Peptides mediating interaction networks: new leads at last.

A growing number of protein interactions are found to be mediated by a large globular region in one protein binding to a comparatively short, peptide stretch in another. Regions that bind a common protein often show a similar sequence pattern or linear motif that mediates the binding. The past year has seen reports of new techniques that can uncover these motifs directly from interaction data. These studies have suggested that the fraction of interactions mediated by these regions is greater than previously anticipated. Concurrently, other work has demonstrated that it is possible to target these interactions using small molecules. Together these developments hold great promise for future efforts to target chemically precise details of complex systems.

GlobPlot: Exploring protein sequences for globularity and disorder.

A major challenge in the proteomics and structural genomics era is to predict protein structure and function, including identification of those proteins that are partially or wholly unstructured. Non-globular sequence segments often contain short linear peptide motifs (e.g. SH3-binding sites) which are important for protein function. We present here a new tool for discovery of such unstructured, or disordered regions within proteins. GlobPlot (http://globplot.embl.de) is a web service that allows the user to plot the tendency within the query protein for order/globularity and disorder. We show examples with known proteins where it successfully identifies inter-domain segments containing linear motifs, and also apparently ordered regions that do not contain any recognised domain. GlobPlot may be useful in domain hunting efforts. The plots indicate that instances of known domains may often contain additional N- or C-terminal segments that appear ordered. Thus GlobPlot may be of use in the design of constructs corresponding to globular proteins, as needed for many biochemical studies, particularly structural biology. GlobPlot has a pipeline interface--GlobPipe--for the advanced user to do whole proteome analysis. GlobPlot can also be used as a generic infrastructure package for graphical displaying of any possible propensity.

In Vivo Tracking of Human Hematopoiesis Reveals Patterns of Clonal Dynamics during Early and Steady-State Reconstitution Phases.

Hematopoietic stem/progenitor cells (HSPCs) are capable of supporting the lifelong production of blood cells exerting a wide spectrum of functions. Lentiviral vector HSPC gene therapy generates a human hematopoietic system stably marked at the clonal level by vector integration sites (ISs). Using IS analysis, we longitudinally tracked >89,000 clones from 15 distinct bone marrow and peripheral blood lineages purified up to 4 years after transplant in four Wiskott-Aldrich syndrome patients treated with HSPC gene therapy. We measured at the clonal level repopulating waves, populations' sizes and dynamics, activity of distinct HSPC subtypes, contribution of various progenitor classes during the early and late post-transplant phases, and hierarchical relationships among lineages. We discovered that in-vitro-manipulated HSPCs retain the ability to return to latency after transplant and can be physiologically reactivated, sustaining a stable hematopoietic output. This study constitutes in vivo comprehensive tracking in humans of hematopoietic clonal dynamics during the early and late post-transplant phases.

Linear motifs: evolutionary interaction switches.

Linear motifs are short sequence patterns associated with a particular function. They differ fundamentally from longer, globular protein domains in terms of their binding affinities, evolution and in how they are found experimentally or computationally. In this Minireview, we discuss various aspects of these critically important functional regions.

Lentiviral hematopoietic stem cell gene therapy benefits metachromatic leukodystrophy.

Metachromatic leukodystrophy (MLD) is an inherited lysosomal storage disease caused by arylsulfatase A (ARSA) deficiency. Patients with MLD exhibit progressive motor and cognitive impairment and die within a few years of symptom onset. We used a lentiviral vector to transfer a functional ARSA gene into hematopoietic stem cells (HSCs) from three presymptomatic patients who showed genetic, biochemical, and neurophysiological evidence of late infantile MLD. After reinfusion of the gene-corrected HSCs, the patients showed extensive and stable ARSA gene replacement, which led to high enzyme expression throughout hematopoietic lineages and in cerebrospinal fluid. Analyses of vector integrations revealed no evidence of aberrant clonal behavior. The disease did not manifest or progress in the three patients 7 to 21 months beyond the predicted age of symptom onset. These findings indicate that extensive genetic engineering of human hematopoiesis can be achieved with lentiviral vectors and that this approach may offer therapeutic benefit for MLD patients.

Lentiviral hematopoietic stem cell gene therapy in patients with Wiskott-Aldrich syndrome.

Wiskott-Aldrich syndrome (WAS) is an inherited immunodeficiency caused by mutations in the gene encoding WASP, a protein regulating the cytoskeleton. Hematopoietic stem/progenitor cell (HSPC) transplants can be curative, but, when matched donors are unavailable, infusion of autologous HSPCs modified ex vivo by gene therapy is an alternative approach. We used a lentiviral vector encoding functional WASP to genetically correct HSPCs from three WAS patients and reinfused the cells after a reduced-intensity conditioning regimen. All three patients showed stable engraftment of WASP-expressing cells and improvements in platelet counts, immune functions, and clinical scores. Vector integration analyses revealed highly polyclonal and multilineage haematopoiesis resulting from the gene-corrected HSPCs. Lentiviral gene therapy did not induce selection of integrations near oncogenes, and no aberrant clonal expansion was observed after 20 to 32 months. Although extended clinical observation is required to establish long-term safety, lentiviral gene therapy represents a promising treatment for WAS.

An allosteric inhibitor of substrate recognition by the SCF(Cdc4) ubiquitin ligase.

The specificity of SCF ubiquitin ligase-mediated protein degradation is determined by F-box proteins. We identified a biplanar dicarboxylic acid compound, called SCF-I2, as an inhibitor of substrate recognition by the yeast F-box protein Cdc4 using a fluorescence polarization screen to monitor the displacement of a fluorescein-labeled phosphodegron peptide. SCF-I2 inhibits the binding and ubiquitination of full-length phosphorylated substrates by SCF(Cdc4). A co-crystal structure reveals that SCF-I2 inserts itself between the beta-strands of blades 5 and 6 of the WD40 propeller domain of Cdc4 at a site that is 25 A away from the substrate binding site. Long-range transmission of SCF-I2 interactions distorts the substrate binding pocket and impedes recognition of key determinants in the Cdc4 phosphodegron. Mutation of the SCF-I2 binding site abrogates its inhibitory effect and explains specificity in the allosteric inhibition mechanism. Mammalian WD40 domain proteins may exhibit similar allosteric responsiveness and hence represent an extensive class of druggable target.

A global protein kinase and phosphatase interaction network in yeast.

The interactions of protein kinases and phosphatases with their regulatory subunits and substrates underpin cellular regulation. We identified a kinase and phosphatase interaction (KPI) network of 1844 interactions in budding yeast by mass spectrometric analysis of protein complexes. The KPI network contained many dense local regions of interactions that suggested new functions. Notably, the cell cycle phosphatase Cdc14 associated with multiple kinases that revealed roles for Cdc14 in mitogen-activated protein kinase signaling, the DNA damage response, and metabolism, whereas interactions of the target of rapamycin complex 1 (TORC1) uncovered new effector kinases in nitrogen and carbon metabolism. An extensive backbone of kinase-kinase interactions cross-connects the proteome and may serve to coordinate diverse cellular responses.