Invited Speakers

An AI solution to the protein folding problem: what is it, how did it happen, and some implications – Professor John Moult (University of Maryland)

Abstract: Computing the three-dimensional structure of a protein molecule from its amino acid sequence is a long-standing grand challenge problem.  Results from the recent Critical Assessment of Structure Prediction (CASP14) experiment show that new deep-learning methods have now provided a dramatic solution, with many computed structures comparable, likely sometimes better, representations of in vivo protein structures to those obtained with state-of-the-art experimental techniques of crystallography and cryo-electron microscopy.  These models have already demonstrated an ability to solve problematic crystal structures, and the results suggest the methods will be successfully applied to other areas of structural biology and more generally.

Bio: John Moult is a Fellow at the Institute for Bioscience and Biotechnology Research and Professor in the Department of Cell Biology and Molecular Genetics at the University of Maryland. He is co-founder and Chair of CASP (Critical Assessment of Protein structure Prediction), an organization that conducts large-scale community experiments in protein structure modeling, and joint founder of CAGI, a sister organization for advancing genome interpretation. He is an ex-crystallographer turned computational biologist. His research interests include the relationship between genetic variation and human disease, disease mechanisms, protein structure, and different ways of doing science.  
BSc Physics, University of London 1965
D.Phil Molecular Biophysics, University of Oxford 1970

Protein-Ligand Structure Prediction for GPCR Drug Design – Dr Chris De Graaf (Sosei Heptares)

From GPCR Structure Prediction to Structural GPCR-Ligand Interaction Prediction

– The conserved TM helical fold of G Protein-Coupled Receptors (GPCRs) and progress in GPCR structural biology continues to provide homology modeling templates for protein structure prediction.

–  Novel structures of GPCR-ligand complexes solved at Sosei Heptares and elsewhere continue to reveal a diversity of protein-ligand binding sites and binding modes that are challenging to predict.

Appreciating the Devil’s in the Details of Structure-Based GPCR Drug Design

– Novel structural insights into the GPCRome can be complemented by pharmacological, biophysical, and computational studies and data to identify and predict structural determinants of ligand-receptor binding and selectivity.

– Orthogonal physics-based (Molecular Dynamics, e.g. Free Energy Perturbation FEP+, WaterMap from Schrödinger) and empirical (e.g. GRID and WaterFLAP from Molecular Discovery) structure-based drug design methods to target lipophilic hotspots and modulate water networks across GPCR families.

Chemogenomic View to Navigate Structural GPCR-Ligand Interaction Space

– Integrated GPCR-ligand chemogenomics views that combine structural, pharmacological, and chemical data allow the exploration of receptor-ligand interaction space for structure-based GPCR drug design.

Bio: Dr. Chris de Graaf is Head of Computational Chemistry at Sosei Heptares, an international biopharmaceutical group focused on the design and development of new medicines originating from its proprietary GPCR-targeted StaR® technology and Structure-Based Drug Design platform capabilities ( In this role Chris is leading the development and application of structural cheminformatics and computer-assisted drug design approaches across the GPCRome to help Sosei Heptares advance a broad and deep pipeline of partnered and in-house drug candidates in multiple therapeutic areas including neurology, immuno-oncology, gastroenterology, inflammation and rare/specialty diseases.

Lessons learned from generative models of biological sequences – Professor Aleksej Zelezniak (Chalmers University of Technology)
Abstract: De novo protein design for catalysis of any desired chemical reaction is a long-standing goal in protein engineering because of the broad spectrum of technological, scientific and medical applications. However, mapping protein sequence to protein function is currently neither computationally nor experimentally tangible. Here, I will present a recently develop ProteinGAN approach, a self-attention-based variant of the generative adversarial network that is able to ‘learn’ natural protein sequence diversity and enables the generation of functional protein sequences. ProteinGAN learns the evolutionary relationships of protein sequences directly from the complex multidimensional amino-acid sequence space and creates new, highly diverse sequence variants with natural-like physical properties. Using malate dehydrogenase (MDH) as a template enzyme, we show that 24% (13 out of 55 tested) of the ProteinGAN-generated and experimentally tested sequences are soluble and display MDH catalytic activity in the tested conditions in vitro, including a highly mutated variant of 106 amino-acid substitutions. ProteinGAN therefore demonstrates the potential of artificial intelligence to rapidly generate highly diverse functional proteins within the allowed biological constraints of the sequence space.

Talk is based on recently published work:
Repecka, D., Jauniskis, V., Karpus, L. et al. Expanding functional protein sequence spaces using generative adversarial networks. Nat Mach Intell 3, 324–333 (2021).

Bio: Aleksej Zelezniak is a tenured Associate Professor, SciLifeLab fellow at the Chalmers University of Technology, Gothenburg, Sweden. He graduated MSc degree in Bioinformatics from the Technical University of Denmark with PhD at the European Molecular Biology Laboratory (EMBL) in Heidelberg, Germany developing network-based omics data integration methods for studying metabolic networks. For his postdoctoral training as an EMBO fellow, he joined the lab of Dr Markus Ralser at the University of Cambridge and the Francis Crick Institute, London, developing applications of machine learning to high-throughput mass spectrometry data. From 2017 he leads an independent research group developing machine learning approaches for de novo protein and DNA designs for biotechnology and synthetic biology applications.

Designing molecular models by machine learning and experimental data – Professor Cecilia Clementi (Freie Universität Berlin)

Abstract: The last years have seen an immense increase in high-throughput and high-resolution technologies for experimental observation as well as high-performance techniques to simulate molecular systems at a microscopic level, resulting in vast and ever-increasing amounts of high-dimensional data. However, experiments provide only a partial view of macromolecular processes and are limited in their temporal and spatial resolution. On the other hand, atomistic simulations are still not able to sample the conformation space of large complexes, thus leaving significant gaps in our ability to study molecular processes at a biologically relevant scale. We present our efforts to bridge these gaps, by exploiting the available data and using state-of-the-art machine-learning methods to design optimal coarse models for complex macromolecular systems. We show that it is possible to define simplified molecular models to reproduce the essential information contained both in microscopic simulation and experimental measurements.

Bio: Cecilia Clementi is Einstein Professor of Physics at Freie Universität (FU) Berlin, Germany. She joined the faculty of FU in June 2020 after 19 years as a Professor of Chemistry at Rice University in Houston, Texas. Cecilia obtained her Ph.D. in Physics at SISSA and was a postdoctoral fellow at the University of California, San Diego, where she was part of the La Jolla Interfaces in Science program. Her research focuses on the development and application of methods for the modeling of complex biophysical processes, by means of molecular dynamics, statistical mechanics, coarse-grained models, experimental data, and machine learning. Cecilia’s research has been recognized by a National Science Foundation CAREER Award (2004), and the Robert A. Welch Foundation Norman Hackerman Award in Chemical Research (2009). Since 2016 she is also a co-Director of the National Science Foundation Molecular Sciences Software Institute.

DeepDock: a deep learning approach to predict ligand binding conformations – Dr Oscar Méndez-Lucio (The Janssen Pharmaceutical Companies of Johnson & Johnson)

Abstract: Understanding the interactions formed between a ligand and its molecular target is key to guide the optimization of molecules. Different experimental and computational methods have been key to understand better these intermolecular interactions. In this talk I will describe DeepDock,  a method based on deep learning that is capable of predicting the binding conformations of ligands to protein targets. Overall, this method performs similar or better than well-established scoring functions for docking and screening tasks. Result presented in this talk are an example of how artificial intelligence can be used to improve structure-based drug design.

Bio: Oscar got his PhD in Chemistry (cheminformatics) from the University of Cambridge in 2016. In 2017, he joined Bayer to apply deep learning to their research pipeline involved in the toxicity prediction. Currently, he is a scientist at Janssen Pharmaceuticals using artifical intlligence to automatically design molecules with improved potency and safety profiles. Oscar has published more than 40 scientific papers including some featured in Nature and Nature Communications.

The “almost druggable” genome – Professor Tudor Oprea (University of New Mexico)

Abstract: This talk will briefly introduce the “Illuminating the Druggable Genome” knowledge management center, with focus on its protein-centric data aggregator, Pharos (, and the DrugCentral online pharmaceutical compendium (  Using Pharos/DrugCentral data, we then examine the question, “what proteins that could potentially be ligandable, are currently not?”, in a disease context.  To do this, we examine proteins available in the RSCB PDB ( – the “PDB-ome” => 347 proteins that lack known ligands; Proteins for which chemical matter is known, N=2644  – the “SAR-ome” => of these, 115 proteins meet the “ligandable” criteria; the “Pocket-ome”, i.e., proteins that have a close – by sequence identity – homologue with known 3D structure, which leads to ~700 ligandable proteins with PDB structures; 180 that have close homologues but lack 3D structures; and N=2623 proteins that could be modeled with reasonable confidence; last but not least, the “Phen-ome”, which looks at this entire list (N = 6742) from the perspective of rare and common diseases, GWAS and mouse phenotype data, etc, and narrows down the previous lists.  The “almost druggable genome” contains 715 ligandable (3D exists) proteins, 180 proteins for which chemical matter is likely to be found, and at least 100 proteins that could be subject to chemical probe optimization.    

Bio: Tudor Oprea is Professor and Chief, Translational Informatics Division, Department of Internal Medicine, UNM School of Medicine, Albuquerque, New Mexico (USA); and Guest Professor at the universities of Gothenburg (Sweden) and Copenhagen (Denmark).  He holds an MD PhD from the University of Medicine and Pharmacy, Timişoara, Romania.  Dr. Oprea co-authored over 260 publications, 10 US patents, and edited 2 books on drug discovery. His current research is to develop validated artificial intelligence models for target selection in drug discovery by combining numerical and text-mined information to model human health. Oprea serves as PI for the Illuminating the Druggable Genome Knowledge Management Center.

General Effects of AI on Drug Discovery – Dr Derek Lowe (Novartis)

Abstract: The advent of gradually more effective AI/ML techniques is already having effects on the traditional practices of medicinal chemistry and drug discovery in general. What can we expect as the process goes on, and how will drug discovery scientists have to adjust their thinking and their research roles?

Bio: I’ve been working in biopharma drug discovery since 1989 in a variety of therapeutic areas, and in recent years I’ve focused more on chemical biology. I’m currently a director with the Novartis (NIBR) Chemical Biology and Therapeutics organization. I’m also known for my web site, “In the Pipeline”, hosted at Science Translational Medicine, which is (I believe) now the oldest/longest-running science blog on the internet (since 2002).

Open Access Data: A Cornerstone for Artificial Intelligence Approaches to Protein Structure Prediction Professor Stephen Burley (RCSB PDB, Rutgers University, UCSD)

Abstract: The Protein Data Bank (PDB) was established in 1971 to archive three-dimensional (3D) structures of biological macromolecules as a public good.  Fifty years later, the PDB is providing millions of data consumers around the world with open access to more than 175,000 experimentally determined structures of proteins and nucleic acids (DNA, RNA) and their complexes with one another and small-molecule ligands. PDB data users are working, teaching, and learning in fundamental biology, biomedicine, bioengineering, biotechnology, and energy sciences. They also represent  the fields of agriculture, chemistry, physics and materials science, mathematics, statistics, computer science, and zoology, and even the social sciences. The enormous wealth of 3D structure data stored in the PDB has underpinned significant advances in our understanding of protein architecture, culminating in recent breakthroughs in protein structure prediction accelerated by artificial intelligence approaches and machine learning methods.

Bio: Stephen Burley is an expert in structural biology, proteomics, data science, structure/fragment-based drug discovery, and clinical medicine/oncology. Burley currently serves as University Professor and Henry Rutgers Chair, Founding Director of the Institute for Quantitative Biomedicine, and Director of the RCSB Protein Data Bank at Rutgers, The State University of New Jersey. He is also a Member of the Rutgers Cancer Institute of New Jersey, where he Co-Leads the Cancer Pharmacology Research Program. From 2008 to 2012, Burley was a Distinguished Lilly Research Scholar in Eli Lilly and Co. Prior to joining Lilly, Burley served as the Chief Scientific Officer and Senior Vice President of SGX Pharmaceuticals, Inc., a publicly traded biotechnology company that was acquired by Lilly in 2008. Until 2002, Burley was the Richard M. and Isabel P. Furlaud Professor at The Rockefeller University and an Investigator in the Howard Hughes Medical Institute. He has authored/coauthored more than 300 scholarly scientific articles in top journals including Science, Science Advances, Cell, Molecular Cell, Structure, Nature, Nature Biotechnology, Nature Chemical Biology, Nature Genetics, Nature Methods, Nature Oncogene, Nature Scientific Data, Nature Structural and Molecular Biology, Nucleic Acids Research, Proceedings of the National Academy of Sciences, Journal of the American Chemical Society, Journal of Molecular Biology, PLOS Computational Biology, and Biochemistry and Molecular Biology Education. Following undergraduate training in physics and applied mathematics, Burley received an M.D. degree from Harvard Medical School in the joint Harvard-MIT Health Sciences and Technology Program and, as a Rhodes Scholar, received a D.Phil. in Structural Biology from Oxford University. He trained in internal medicine at the Brigham and Women’s Hospital in Boston and did postdoctoral work with Gregory A. Petsko at the Massachusetts Institute of Technology and Nobel Laureate William N. Lipscomb, Jr. at Harvard University. With William J. Rutter and others at the University of California San Francisco and Rockefeller, Burley co-founded Prospect Genomics, Inc., which was acquired by SGX in 2001. He is a Fellow of the Royal Society of Canada, the New York Academy of Sciences, and the American Crystallographic Association, and recipient of a Doctor of Science (Honoris causa) from his alma mater the University of Western Ontario, from which he received a B.Sc. in Physics in 1980.

Submitted Speakers

So you predicted a protein structure – What now? – Dr Thomas Steinbrecher (Schrödinger)

Abstract: Recent advances in technologies like cryoEM structure resolution and protein de novo folding prediction have resulted in a wealth of macromolecular structures that have not been resolved to the level of detail a high-resolution X-ray crystal structure could provide. Taking full advantage of these structures for rational drug design would benefit from additional validation and refinement. In this presentation, we investigate if computational refinement and structure-based modeling methods can be utilized to generate reliable complex poses. We present a solution to the induced fit docking problem for protein−ligand binding by combining ligand-based pharmacophore docking, rigid receptor docking, and protein structure prediction with explicit solvent molecular dynamics simulations. This methodology succeeded in determining protein−ligand binding modes with a root-mean-square deviation within 2.5 Å compared to experiment in over 90% of cross-docking cases in our testing. Applications of the predicted ligand-receptor structure in free energy perturbation calculations for additional validation is demonstrated.

Bio: Thomas Steinbrecher studied Chemistry at the University of Freiburg in Germany and earned a diploma with distinction in Physical Chemistry. He completed a Ph.D. thesis on “Computer Simulations of Protein-Ligand Interactions” in 2005. He joined the developer team of the Amber MD package as a Postdoc at the Scripps Research Institute in San Diego and Rutgers University in New Jersey. The work focus was on efficient free energy calculation methods and QM/MM simulations of charge transfer. After returning to Germany in 2008, Thomas established a junior research group at the Karlsruhe Institute of Technology, working on fast electron transfer phenomena in DNA and proteins. He joined Schrodinger in 2013 where he was responsible for the large scale application of free energy calculation methods in pharmaceutical drug design. Since 2017, he heads the Applications Science Department for Europe and supports customers in employing Schrödinger’s Drug Discovery Technology Platform for their research.

Deep Learning enhanced prediction of protein structure and dynamics – Dr Martina Audagnotto (AstraZeneca)

Abstract: Proteins exist in several different conformations. These structural changes are often associated with fluctuations at the residue level. Recent findings showed that co-evolutionary analysis coupled with machine-learning techniques improved the prediction precision by providing quantitative distance predictions between pairs of residues. The predicted statistical distance distribution from the Multi Sequence Analysis (MSA) revealed the presence of different local maxima suggesting the flexibility of key residue pairs. Here we investigate the ability of the residue-residue distance prediction to provide insights into the protein conformational ensemble. We combine deep learning approaches with mechanistic modeling to a set of proteins that experimentally showed conformational changes. The predicted protein models were filtered based on their energy scored, RMSD clustered, and the centroids locally refined. The models were compared to the experimental-Molecular Dynamics (MD) relaxed structure by analyzing the backbone residue torsional distribution and the sidechains orientations. Our pipeline not only consents us to retrieve the global experimental folding but also the experimental structural dynamics due to local and global conformational changes. Based on the insight of this study we are proposing a protocol that allows the in-silico investigation of protein dynamics suited for pharmacological research on catalysis and molecular recognition.

Bio: Martina completed her M.Sc. in Chemistry at the University of Turin, studying the effect of the adsorption of amino acids on titanium dioxide using quantum mechanics approaches. Afterwards, she pursued a PhD in the field of Molecular Dynamics at École Polytechnique de Lausanne (EPFL) under Prof. Dal Peraro’s supervision. Combing X-ray experiments and Molecular Dynamics simulations, Martina investigated the membrane-protein interplay in modeled physiological conditions. Her work highlighted the importance of applying a multilevel approach to achieve a comprehensive picture of biological systems and understanding the dynamic interactions and subsequent events that occur in cells. At the University of California San Diego (UCSD) under the supervision of Prof. Amaro, Prof. Villa and Prof. Taylor, Martina worked as a postdoctoral fellow on an interdisciplinary project to investigate the LRRK2 familial mutations and their association with Parkinson’s Disease. By combining in situ cryo-electron tomography (ET) density map with X-ray structure and homology models, she revealed the atomistic architecture of LRRK2 protein and their organization around the microtubule providing the starting point for future medicinal chemistry studies. Martina is currently working on proteins structure folding prediction methods at AstraZeneca in the team of Christian Tyrchan. By combining deep learning approaches with mechanistic modeling she retrieved not only the global experimental folding but also the experimental structural dynamics due to local and global conformational changes. Based on the insight of this study the proposing protocol will allow the in-silico investigation of protein dynamics suited for pharmacological research on catalysis and molecular recognition.

Fireflies-Lévy Flights algorithm for peptides conformational optimization – Dr Zied Hosni (University of Sheffield)

Abstract: Over the last 50 years, several algorithms and approaches were introduced and improved to tackle the challenges of exploring a large and multidimensional conformational space. Optimisation algorithms are frequently used to guide the search in a conformational space of complex molecules such as proteins. It is a crucial step to access molecular properties corresponding to the most stable conformer. The optimisers are usually buried in docking software with limited tuning possibilities. We implement a Fireflies algorithm with Lévy flights distribution to search for the lowest energy conformations of peptides. The hyperparameters of this bio-inspired metaheuristics algorithm are tuned and its performance is compared with the state-of-the-art method. Our results show that the Fireflies-Lévy flights algorithm is able to improve upon the genetic algorithm method with fewer energy evaluations. To the best of our knowledge, this is the first cheminformatics application that will open the door to additional nature-inspired metaheuristics to support the conformational analysis of large biomolecules.  

Bio: Zied finished his PhD in the Cronin Group at the University of Glasgow before securing a postdoctoral position in the Bioinformatics Hub in the Centre for Virus Research in Glasgow. During his research experience, he developed the ability to apply his knowledge of computational chemistry and synthesis into practical use in drug discovery, including machine learning tools for polymorphism predictions, artificial intelligence solution development, big data technologies and virtual screening. Before coming to Sheffield, Zied was a research associate in the Centre of Computational Chemistry at Bristol University where he was investigating mechanistic insights of the stereoselectivity in boron-lithium chemistry. He was previously a research associate at Strathclyde Institute of Pharmacy and Biological Sciences at Strathclyde University (UK) where he fully utilised his machine learning for scientific projects, whilst collaborating with several global pharmaceutical companies such as Lilly, AstraZeneca and Novartis, providing the opportunity to liaise with industry professionals and experts.

How good are protein structure prediction methods at predicting folding pathways? – Mr Carlos Outeiral Rubiera (University of Oxford)

Abstract: Deep learning has achieved unprecedented success in predicting a protein’s crystal structure, but whether this achievement relates to a better modelling of the folding process is an open question. In this work, we compare the dynamic pathways from six state-of-the-art protein structure prediction methods to experimental folding data. We find evidence of a weak correlation between simulated dynamics and formal kinetics; however, many of the structures of the predicted intermediates are incompatible with available hydrogen-deuterium exchange experiments. These results suggest that recent advances in protein structure prediction do not provide an enhanced understanding of the principles underpinning protein folding.

Bio: Carlos Outeiral was born and raised in northern Spain, where he completed his baccalaureate (achieving a National Award) and earned a BSc in Chemistry at the University of Oviedo (achieving the honours of valedictorian and extraordinary award). Following internships at the Technical University of Munich (Germany) and Harvard University (US), he completed a MPhil in Chemistry at the University of Manchester (UK). He is currently a final-year PhD candidate at the University of Oxford (UK), where his research studies novel algorithms to simulate protein folding at scale. Some of his work has examined the prospects of quantum computing in computational biology, and developed pipelines for biologically-inspired protein structure prediction. In his free time, Carlos is passionate about deep tech, entrepreneurship and open source software.

Using icospherical input data in machine learning on the protein-binding problem – Dr Ella Gale (University of Bristol)

Abstract: Determining the binding coefficients of ligands to proteins is an essential step in targeted drug development. The 3-dimensional structure of both the protein binding pocket and the ligand is crucial in solving this problem. I will present ICOSPHERER (Icospherical Chemical Objects Surpassing Traditional A.I. Restrictions Through Replacing Existing Representations) a new methodology and software and demonstrate it’s use on the protein binding problem.

Bio: Ella Gale is currently the Machine Learning subject specialist attached to the Technology Enhanced Chemical Synthesis CDT in the School of Chemistry at the University of Bristol. Her current responsibilities include training the CDT students in machine learning, data science, statistics and design of experiments, providing data science and machine learning support to the chemistry department generally and researching machine learning techniques for retrosynthesis and de novo drug design. Dr Gale has has over ten years of experience working across artificial intelligence, computer science and chemistry.

Finding new in silico-based therapeutic strategies for IAHSP – Dr Matteo Rossi Sebastiano (University of Turin)

Abstract: Infantile-onset ascending spastic paralysis (IAHSP) is a neurodegenerative autosomic recessive rare disease which affects less than 50 people worldwide. The pathogenesis starts in early childhood, with a progressive degeneration of the upper spinal motoneuron, progressively hindering deambulation until spread to the upper limbs and to the involuntary musculature(1). As it often occurs for rare diseases, although few interest from the pharma compartment, some information regarding this condition are available from case reports: key events responsible for this condition are mutations to the gene ALS2, which encodes for the cell trafficking-related protein alsin. Nevertheless, the relatively broad mutational landscape and the low number of reported cases still make a complete understanding of the physiopathology and the search for suitable therapeutic strategies pretty challenging. The majority of mutations described in literature result in a truncated form of alsin which is reputed to be degraded, thus depicting a scenario of loss-of-function pathogenesis. Nevertheless, some patients report missense mutation, leading to non-degraded, mutated forms. In those cases, the majority of amino-acid (aa) substitutions occur in the N-terminal RLD domain, essential for alsin localization to the plasma membrane and eventually to early and late endosomes upon activation of the RAC1 pathway. In endosomes, alsin binds to the small GTPase Rab5 and performs a guanosin-exchange factor activity (GEF) through its C-terminal VPS9 domain2. This pathway is reputed to be the major strategy that mammalian cells follow, in order to assemble endosomes and exchange materials within the cell architecture. In dimensionally important cells such as motoneurons, coordinated and efficient cell trafficking results crucial for correct development and function maintenance. Alsin exists in cytoplasmic solution as tetramer, firstly assembled by parallel dimerization through the VPS9 domain and subsequently by interaction of two dimers through their DH/PH domain, located upwards of the VPS9 region2. The first challenge that such a broad mutational landscape offers is that different mutations correspond to different multimers. These states do not just affect stability and solubility, but also subcellular localization and GEF activity. To make this situation more challenging, there is no experimentally-resolved 3D structure of alsin and a homology modeling effort to build the whole protein seems questionable because of the lack of a reliable template. In contrast with the majority of reports, here we present a patient case harboring two alsin mutations in the C-terminal region: one allele translates a frame-shifted, truncated form which gets degraded. The other allele is harboring the R1611W aa substitution in the VPS9 domain. With the aid of in silico computational tools, we managed to predict the 3D structure of normal and mutated forms of this domain. Moreover, we characterized physiologic and pathologic dimerization modes, discovering that mutated VPS9 preferentially forms an antiparallel dimer by interacting with the aforementioned RLD domain. We could link this discovery to the experimentally-determined loss of tetrameric aggregation and, more important, to the incorrect endosome localization. This finding corroborates and gives a mechanistic explanation for the experimentally-characterized reduced Rab5 GEF endosomal activity. Furthermore, we performed an in silico virtual screening, repurposing an already commercialized drug which is able to shield the pathologically-acquired hydrophobic moiety. In our hypothesis, this mechanism of action re-establishes physiological dimerization mode, subcellular localization and Rab5 activity in R1611W-mutated patients. The candidate is currently under pre-clinical testing in an alsin R1611W cellular model. Our hope and the scope of our effort is duplex: first, we want to provide a reliable treatment for alleviating symptoms and disease progression to our patient. Second, we would like to broaden the knowledge in the field and, by integrating in silico and in vitro procedures, establish a lean research pipeline that might once serve as mutation-based platform for individual drug repurposing for the treatment of alsin-related diseases.


  1. Lesca, G. et al. Infantile ascending hereditary spastic paralysis (IAHSP): Clinical features in 11 families. Neurology 60, 674–682 (2003).
  2. Sato, K. et al. Altered oligomeric states in pathogenic ALS2 variants associated with juvenile motor neuron diseases cause loss of ALS2-mediated endosomal function. J. Biol. Chem. 293, 17135–17153 (2018).

Bio: Born in Turin (Italy) 19.06.1990
2009: science high school diploma
2009-2015: Unique cycle master in Pharmaceutical sciences at the University of Turin (Italy), with a thesis integrating computational tools and wet lab techniques aimed to identify the dynamics of a protein-protein interaction and its pharmacologic inhibition. It led to the identification of two lead compounds against HER2+ breast cancer.
2015-2016: Worked as grant researcher at the Biomedizinsk Zentrum, Uppsala Universität (Sweden) with focus on molecular property calculation and chemical space definition for beyond the Rule of five compounds. It led to a milestone publication defining innovative polar surface areas as tool for permeability predictions of drugs in the beyond the Rule of five chemical space.
2016-2020: PhD in biomedical sciences at the Universität Bern (Switzerland) with a thesis investigating fatty acid metabolism in pancreatic ductal adenocarcinoma. Here I identified a key fatty acid-activator and several druggable downstream mechanisms at the crossover between tumor metabolism and immune resistance.
2020-present: Post-doc at the University of Turin (Italy) with focus on structural biology of Alsin, drug repurposing for neurological disorders and PROTAC® drugs chemical space definition.