Joint Integrative Computational Biology workshop and CAPRI Meeting

Session

CAPRI

14 Feb 2024, 14:15

IBS seminar room (EPN Campus)

57. CAPRI 2024: Pre- and post-Alphafold protein docking

Marc Lensink (CNRS)

14/02/2024, 14:15

Talk

55. Integrated pipeline for protein docking with extended and enhanced Alphafold-based modeling methods

Daisuke Kihara (Purdue University)

14/02/2024, 15:00

Talk

Our group, Kiharalab, participated in both the prediction and scoring stages for complex targets. We combined three components in our pipeline, DistPepFold [1], AFSample [2], and a consensus-based score called ranksum we developed for our LZerD protein docking program [3]. For peptide docking, we used our new approach, DistPepFold. DistPepFold improves protein-peptide complex docking using an...

36. Towards integrative use of pyDock and AI-based modeling in 8th CAPRI

Juan Fernandez-Recio (ICVV-CSIC and BSC)

14/02/2024, 16:15

Talk

We have participated, both as predictors and as scorers, in all proposed targets of the 8th CAPRI edition. Here we will focus on the 11 purely CAPRI targets, that is, excluding the CASP-CAPRI joint rounds 46, 50 and 54, already evaluated and discussed in the respective CASP meetings, and the 4 targets in round 51 related to COVID19, with no available structure for evaluation. These 11 targets...

35. Prediction and refinement of protein assemblies with ClusPro, Alphafold and Molecular Dynamics.

Dima Kozakov (Stony Brook University)

14/02/2024, 16:45

Talk

In the latest CAPRI round our group used a combination of Alphafold-Multimer (AFM), the ClusPro webserver for docking, and Molecular Dynamics based sampling to refine models for small targets. Assembly prediction was based on a two-stage methodology, in which we first generate an ensemble of initial models using AlphaFold-Multimer using standard protocol for MSA generation. We stop the...

19. MassiveFold: optimized massive sampling with AlphaFold2

Guillaume Brysbaert (CNRS University of Lille)

14/02/2024, 17:15

Talk

Massive sampling with AlphaFold-multimer(1,2) showed impressive results for structural prediction of macromolecular assemblies at CASP15-CAPRI(3). Generating a very large number of predictions (>1000) and pushing their diversity by playing with the neural network model versions, the number of recycle steps, the use of templates or not and the activation of the dropout in the Evoformer and in...

63. From Interaction Prediction to Sequence Design: Unveiling PeSTo's Potential in Structural Biology

Lucien Krapp (Laboratory for Biomolecular Modeling, EPFL)

14/02/2024, 17:45

Talk

In the field of structural biology, predicting protein interactions and designing sequences based on backbone scaffolds remain pivotal yet challenging tasks. Built on the same deep learning architectural framework, Protein Structure Transformer (PeSTo) and its derivative, CARBonAra, address these challenges. PeSTo employs geometric transformers to proficiently predict diverse protein binding...

12. Impact of AI-Based Modeling on the Accuracy of Protein Assembly Prediction: The CASP Perspective

Ezgi Karaca (Izmir Biomedicine and Genome Center, DEU)

15/02/2024, 09:00

Talk

In CASP15, 87 predictors submitted around 11,000 models on 41 assembly targets. The community demonstrated exceptional performance in overall fold and interface contact prediction, achieving an impressive success rate of 90% (compared to 31% in CASP14). This remarkable accomplishment is largely due to the incorporation of DeepMind’s AF2-Multimer approach into custom-built prediction pipelines....

51. What have we learned (and memorized) about peptide-mediated interactions in CAPRI and beyond?

Ora Schueler-Furman (Hebrew University of Jerusalem)

15/02/2024, 09:30

Talk

Since the last CAPRI meeting, much has happened in the modeling field. Deep Learning (DL) has revolutionized structure prediction, and we are only beginning to grasp the fruits, as well as the new challenges uncovered in this new era.
I will describe different approaches that we developed and applied to address CAPRI challenges, and how they helped us shape and improve our ability to model,...

31. HADDOCK in latest CAPRI rounds

Dr Marco Giulini (Utrecht University), Dr Victor Reys (Utrecht University)

15/02/2024, 10:00

Talk

The HADDOCK team have participated as human and webserver predictor and/or scorer in last CAPRI rounds (47-55), mainly relying on the use of our integrative modelling software HADDOCK[1] which is able to use user-provided information to guide the docking process. An important element of HADDOCK is its scoring function, computed from the weighted sum of 4 energetical terms (VdW, Electrostatics,...

53. Assessing the impact of single-point mutations and alternative splicing-induced variations on protein-protein interactions

Elodie Laine

15/02/2024, 11:00

Talk

Beyond determining the 3D arrangement of interacting protein partners, assessing the impact of sequence variations on binding affinity and specificity is of utmost importance. I will present two methods we developed to address this question. The first method, DLA-Mutation [1], relies on geometric deep learning for predicting mutation-induced binding affinity changes. It exploits and contrasts...

58. The Expanding Horizon of Protein Interactions

Shoshana Wodak (VIB-VUB Center doe Structural Biooogy, Brussels Belgium)

15/02/2024, 11:30

Talk

20. Multistage Docking Approach for Protein-RNA Interactions Prediction in CAPRI and CASP

Shan Chang (Jiangsu University of Technology)

15/02/2024, 14:00

Talk

Protein-RNA interactions and recognition are essential in gene expression, regulation of transcription, and other biological processes. Prediction of protein and RNA is also a new challenge category for CAPRI and CASP. In this work, we proposed a multistage docking protocol called CoDockPR, which integrates the shape complementarity, knowledge-based scoring functions, and interface similarity...

26. An Iteratively Derived Knowledge-based Scoring Function at Atomic Level for Protein-DNA Complexes Evaluations

XIaoqin Zou (University of Missouri - Columbia)

15/02/2024, 14:30

Talk

Protein-DNA interactions play a significant role in biological processes and drug design owing to their prevalence. Computational methods for predicting protein-DNA complex structures serve as a valuable alternative to experimental methods, which, although more accurate, are also time-consuming and resource-intensive. The established framework for predicting protein-protein complex structures...

29. FTDMP: a framework for protein-protein, protein-DNA and protein-RNA docking and scoring

Rita Banciul (Vilnius University), Justas Dapkunas (Vilnius University)

15/02/2024, 15:00

Talk

Understanding the functions of protein-protein and protein-nucleic acid complexes relies on the knowledge of their 3D structures that can either be solved experimentally or predicted computationally. While AlphaFold has revolutionized protein structure prediction, challenges remain, particularly in modeling antibody-antigen interactions, protein-nucleic acid complexes, and proteins lacking...

49. Interpretable Affinity Prediction and Generative Design for Protein-Ligand Interactions

Yang Shen (Texas A&M University)

15/02/2024, 16:15

Talk

In this talk I will introduce our recent works on machine learning for protein-ligand interactions, including explainable prediction of protein-ligand binding affinity and structure-based de novo ligand design.

First, as deep learning methods for modeling protein-ligand interactions are increasingly improving their accuracy, their interpretability is often under-explored. We had...

59. Automated protein-to-Structure pipelines for new structures and X-ray based ligand screening at EMBL Grenoble. Experience from our first collaboration with CAPRI

José Márquez (EMBL Grenoble)

15/02/2024, 16:45

Talk

13. Shape retrieval methods for the classification of protein surfaces

Grisell Diaz Leines (EMBL-EBI)

16/02/2024, 09:00

Talk

Macromolecular complexes play a crucial role in almost all biological functions in living cells. Elucidating structural features in proteins is essential to understanding their underlying functions and binding activity. With the rapid accumulation of protein-structure data through AI methods and experimental techniques, such as cryo-electron microscopy, there is a growing demand for efficient...

25. High-throughput prediction of ATP synthase rotor ring stoichiometries

Ivan Gushchin (Moscow Institute of Physics and Technology)

16/02/2024, 09:30

Talk

Rotary ATP synthases are large enzyme complexes present in every living cell. They consist of a transmembrane and a soluble domain, each comprising multiple subunits. The transmembrane part contains an oligomeric rotor ring (c-ring), whose stoichiometry defines the ratio between the number of synthesized ATP molecules and the number of ions transported through the membrane. Here, we present...

5. Unsupervised Machine Learning and Phase Space Reduction: A Robust and Generalisable Approach for Concurrently Solving the Protein Complex Conformation Classification and Quantification Problems.

Dr Daniel Celis Garza (CCP4, Research Complex at Harwell, STFC Rutherford-Appleton Laboratory, UK)

16/02/2024, 10:30

Talk

The understanding of biochemical processes and the machinery of life hinges on comprehending the structural aspects of macromolecular interactions. This requires a systematic approach to analysing the vast manifold of macromolecular associations, or complexes, typically determined from crystallographic or electron microscopy experiments. Specific points of interest include similarity...

34. Modeling of immune recognition and protein complexes with deep learning and physics-based docking

Brian Pierce (University of Maryland)

16/02/2024, 11:00

Talk

The Pierce group has utilized a combination of AlphaFold and traditional docking methods, including Rosetta and ZDOCK, to model protein complexes in recent CAPRI and CASP/CAPRI rounds. This has led to success for several challenging targets, as well as useful lessons learned for prospective modeling efforts. Our group has been particularly focused on the utilization and adaptation of AlphaFold...