Asst. Prof. Gangqing Hu

West Virginia University, USA

Dr. Gangqing Hu received his Ph.D. education in molecular biology, bioinformatics, and genomics at Peking University China. After graduation in 2009, he joined Dr. Keji Zhao's lab as a Post-Doc Fellow at National Heart, Lung, and Blood Institute (NHLBI), NIH USA, and developed expertise in chromatin biology. In 2013, he received a promotion to a staff scientist in NHLBI and in the 2019 summer joined the Department of Microbiology, Immunology, and Cell Biology, West Virginia University as a tenure track assistant professor. In the last decade, he has extensive collaborations with bench scientists with many broad-impact publications (Cell 2012; Nature Immunology 2013, 2016; Immunity 2012, 2014, 2018, 2019, 2020, 2022; Nature 2010,2018; Biomaterials 2020; Cancer Res 2020; Oncogene 2021; PNAS 2021, etc). The current research interests in Dr. Hu’s lab are about epigenetic mechanisms of drug resistance of hematological cancers in the context of the bone marrow microenvironment.

Speech Title: "Bone Marrow Stroma-Induced Transcriptome and Regulome Signatures of Multiple Myeloma"

Abstract: Multiple myeloma (MM) is a hematological cancer with inevitable drug resistance. MM cells interacting with bone marrow stromal cells (BMSCs) undergo substantial changes in transcriptome and develop de novo multi-drug resistance. As a critical component in transcriptional regulation, how the chromatin landscape is transformed in MM cells exposed to BMSCs and contributes to the transcriptional response to BMSCs remains elusive. We profiled the transcriptome and regulome for MM cells using a transwell coculture system with BMSCs. The transcriptome and regulome of MM cells from the upper transwell resembled MM cells that coexisted with BMSCs from the lower chamber but were distinctive to monoculture. BMSC-induced genes were enriched in the JAK2/STAT3 signaling pathway, unfolded protein stress, signatures of early plasma cells, and response to proteasome inhibitors. Genes with increasing accessibility at multiple regulatory sites were preferentially induced by BMSCs and were enriched in functions linked to responses to drugs and unfavorable clinic outcomes. We proposed JUNB and ATF4::CEBPβ as candidate transcription factors (TFs) that modulate the BMSC-induced transformation of the regulome linked to the transcriptional response. Together, we characterized the BMSC-induced transcriptome and regulome signatures of MM cells to facilitate research on epigenetic mechanisms of BMSC-induced multi-drug resistance in MM.

Asst. Prof. Jin Yu

University of California, USA

Jin Yu, PhD, Assistant Professor at Department of Physics & Astronomy at UC Irvine. She had BS and MS from Tsinghua University, and obtained her PhD in physics at University of Illinois at Urbana-Champaign, working on theoretical and computational biophysics. Dr Yu then received the UC Berkeley Chancellor’s postdoctoral fellowship and conducted researches on physical and mathematical modeling of bimolecular machines. She later joined the Beijing Computational Science Research Center and worked as a principal investigator in the Complex System Research Division, and then moved to UC Irvine in 2019, where she continued her interdisciplinary researches on computational biophysics, with a focus on protein machinery in genetic regulation. Dr Yu has a joint appointment with the Department of Chemistry, and an affiliation to the NSF-Simons Center of Multiscale Cell Fate Research.

Speech Title: "Protein Search Dynamics and Profiling along DNA"

Abstract: Using molecular modeling and structure-based simulation from atomic to coarse-grained (CG) level, along with physical stochastic approaches, we have recently re-examined transcription factor (TF) protein diffusional search along DNA. We demonstrated at atomic scale the collective hydrogen bonding dynamics at protein-DNA interface during spontaneous protein stepping, and found biased DNA strand association for the small domain protein tracking along DNA groove. Such a bias become highly prominent upon specific DNA sequence binding, in coordination with notable reorientation of the domain protein on the DNA. Processive diffusional motions of the TF protein along DNA can be further sampled in the CG simulations, used as physical computational assay, revealing statistics on the regular sliding, stochastic variations, coordinated stepping dynamics of dimeric TF, and even the dimer dissociation. In addition, we also modeled protein diffusion with dissociation from DNA, physically and mathematically, connecting diffusion and binding free energetics of TF protein along DNA for real-time dynamics detection and long-time ensemble profiling.

Assoc. Prof. Jun Hu

Zhejiang University of Technology, China

Jun Hu, Ph.D., associate professor, secretary-general of the Artificial Intelligence Special Committee of Bioinformatics Society of Zhejiang Province, his research field is bioinformatics. He presided over and participated in 4 projects of the National Natural Science Foundation of China and 2 projects of the Natural Science Foundation of Zhejiang Province, developed 26 bioinformatics-related prediction servers, published more than 30 SCI journal papers, and authorized more than 10 National Invention Patents of China. Instructed graduate students to win 1 silver award in the China College Students' 'Internet+' Innovation and Entrepreneurship Competition, 1 second prize in "Challenge Cup" National Undergraduate curricular academic science and technology competition, and 1 gold award in the Zhejiang Province College Students' 'Internet+' Innovation and Entrepreneurship Competition.

Speech Title: "Prediction of Protein-ATP Binding Sites and Their Docking Poses"

Abstract: Interactions between proteins and ATPs are ubiquitous and indispensable in metabolic processes. The biological functions of these interactions depend on their tertiary structures. Identifying protein-ATP binding sites and poses is significantly important for understanding the mechanism of protein-ATP interaction and designing new drugs. However, it is time-consuming and expensive to measure the binding sites and poses between proteins and ATPs by using the wet-lab experiments. Hence, developing computational methods to accurately identify protein-ATP binding sites and their binding poses is of significant importance for uncovering the mechanisms of protein-ATP interactions. This report will introduce the relevant researches on the prediction of protein-ATP binding sites and poses made by Jun Hu and his research group in recent years.

Prof. Wenfei Li

Nanjing University, China

Wenfei Li received his Ph.D in 2004 from Chinese Academy of Science. He worked as a postdoc at Nanjing University during 2004-2006 and at Kyoto University during 2008-2010. He is now a professor of physics at Nanjing University. His research interests include theoretical and computational biophysics.

Speech Title: "Role of Frustration in Enzyme Catalysis"

Abstract: In this talk, I will introduce our recent efforts towards understanding the physical mechanism of the catalysis of adenylate kinase by using molecular dynamics simulations. We showed that the local frustration at the active site of the enzyme tends to facilitate the rate-limiting product release step of enzymatic cycle, which leads to expedited turnover rate of the enzyme. The role of local frustration in the functional motions of some other proteins will also be discussed.

Prof. Minghui Li

Soochow University, China

Dr. Minghui Li is currently a professor at the School of Biology & Basic Medical Sciences, Soochow University. Dr. Li received her Ph.D. in Physical Chemistry from the State Key Laboratory of Theoretical and Computational Chemistry, Jilin University, China in 2010. Upon completion of her Ph.D., Dr. Li did postdoctoral research for two years at the State University of New York at Buffalo, USA. Then she moved to National Center for Biotechnology Information (NCBI), National Institutes of Health (NIH), and worked as a Postdoctoral Fellow and Research Fellow from 2012 to 2016, respectively. Prof. Minghui Li’s research group studies the effects of mutations on protein function and causes of disease progression, including the development of new computational methods to predict the effects of mutations on protein stability and interactions, to identify cancer-specific driver missense mutations and biomarkers, and to design mutant proteins.

Speech Title: "Machine Learning Models for Predicting the Effects of Missense Mutations on Protein Stability and Interactions"

Abstract: During the last decade, there has been a rapid development of genome-wide techniques along with a significant lowering of the cost of gene sequencing, which generated large amounts of available genomic data. However, the interpretation of genomic data and the prediction of the association of genetic variations with disease phenotypes still require significant improvement. Missense mutations can render protein non-functional and may be responsible for many diseases. Effects caused by missense mutations can be pinpointed by in silico modeling, which makes it more feasible to find a treatment and reverse the effect. Specific human phenotype is associated with protein stability, activity, and interactions with other biomolecules that work together to carry out particular cellular functions. Therefore, estimating and interpreting the missense mutations' effects on proteins and their complexes would provide important clues for identifying functionally important missense mutations, understanding the molecular mechanisms of disease progression, and facilitating treatment and prevention. Herein, we developed computational methods and tools to predict the effects of missense mutations on protein stability and interactions with other molecules including proteins, DNA, RNA, and small molecules. Our models were tested against numerous datasets and show considerable improvement compared to other methods. The developed methods can be used for finding disease driver mutations, providing a molecular understanding of the mutational impacts, designing stable protein or complexes, and discovering new protein-protein/DNA/RNA interaction inhibitors.

Prof. Nikolaos Limnios

University of Technology of Compiègne, France

Nikolaos Limnios, is Full Professor (Exceptional class) at University of Technology of Compiègne (UTC) Sorbonne University, and former Director of the Laboratoty of Applied Mathematics. He has obtained his diploma in 1979 at AUTh Greece, PhD in 1983, and Dr of Sciences (Doctorat d'Etat) in 1991 at UTC France. In 1988 he was appointed assistant professor (Maitre de conferences), and in 1993 a Professor at UTC in the Laboratoty of Applied Mathematics. His research interest include stochastic processes and statistics with applications in biostatistics, in reliability, statistical seismology, stochastic mechanics, etc. He is the Editor-in-Chief of book series “Mathematics and Statistics” in iSTE with J. Wiley and Elsevier. He published more than 150 journal papers and about 15 books on theory and applications of stochastic processes and statistics; amount them the books: Semi-Markov chains and hidden semi-Markov Chains toward applications, Springer, 2008 (with V. S. Barbu); Stochastic Systems in Merging Phase Space, World Scientific, 2005 (with V. S. Koroliuk); Semi-Markov Processes and Reliability, Birkhauser, 2001 (with G. Oprisan).

Speech Title: "Stochastic Modelling of Biological Sequences"

Abstract: Biomolecules have a wide range of sizes and structures and perform a large number of functions. They can be hundreds of thousands of nucleotides, coded by letters, i.e., in the case of DNA we have {A, C, T, G}. In this case, we are interested to find out how many particular ‘words’, i.e., ‘GAATTC’, for ‘EcoRI’ enzyme, can exist in a given sequence, what is the probability distribution of this random number, the average number of letters (nucleotides) between two successive such word in the sequence, etc. In order to answer to the above questions we present models of stochastic sequences of letters via semi-Markov stochastic processes. In order to illustrate the proposed model, we apply it to the bacteriophage DNA sequence.

Asst. Prof. Ning Shen

Zhejiang University, China

Dr. Ning Shen received her B.S. majoring in Biological Science from Fudan University in 2010, followed by a Ph.D. in Pharmacology with a Certificate in Computational Biology from Duke University in 2016. After her PhD, she joint Fulcrum Therapeutics as computational biologist. She joined the Park Lab in the department of Biomedical Informatics at Harvard Medical School as research fellow in 2019. In March 2021, Dr. Shen started her faculty position as principal investigator at Zhejiang University Medical Center in Hangzhou, China. Her research areas focus on pharmacogenomics, bioinformatics and regulatory genomics.

Speech Title: "Detecting Expressed Cancer Somatic Mutations from Single-Cell RNA Sequencing Data"

Abstract: Identifying expressed somatic mutations directly from single-cell RNA sequencing (scRNA-seq) data is challenging but highly valuable. Computational methods have been attempted but no reliable methods have been reported to identify somatic mutations with high fidelity. We present RESA -- Recurrently Expressed SNV Analysis, a computational framework that identifies expressed somatic mutations from scRNA-seq data with high precision. RESA demonstrates average area under the curve (AUC) of 0.9 on independently held out test sets, and achieves average precision of 0.71 when evaluated by bulk whole exome across multiple cancer cell line datasets, which is substantially higher than previous approaches. In addition, RESA detects a median of 201 mutations per cell, 50 times more than what was reported in experimental technologies with simultaneous expression and mutation profiling. Furthermore, applying RESA to scRNA-seq from a melanoma patient, we demonstrate that RESA recovers the known BRAF driver mutation of the sample and melanoma dominating mutational signatures, reveals cluster and stage specific cancer hallmarks and biological processes that carry mutations, and unveils the complex relationship between genomic and transcriptomic intratumor heterogeneity.

Dr. Pengfei Tian

Novozymes A/S, Denmark

I apply machine learning and molecular dynamics simulations to design proteins, with a current focus on enzymes. I earned a Ph.D. in physics from Niels Bohr Institute (Denmark), where I developed Monte Carlo methods for protein folding by combining physical force field and probabilistic model. During my time as a Postdoctoral Fellow at NIH (US), I developed novel approaches for protein design using generative model and studied how proteins fold on the ribosome by coarse-grained molecular dynamics simulations.

Speech Title: "Co-Evolutionary Fitness Landscapes for Sequence Design"

Abstract: Efficient and accurate models to predict the fitness of a sequence would be extremely valuable in protein design. We have explored the use of statistical potentials for the coevolutionary fitness landscape, extracted from known protein sequences, in conjunction with Monte Carlo simulations, as a tool for design. As proof of principle, we created a series of predicted high‐fitness sequences for three different protein folds, representative of different structural classes: the GA (all‐α) and GB (α/β) binding domains of streptococcal protein G, and an SH3 (all‐β) domain. We found that most of the designed proteins can fold stably to the target structure, and a structure for a representative of each for GA, GB and SH3 was determined.

Assoc. Prof. Qingxia Yang

Nanjing University of Posts and Telecommunications, China

Qingxia Yang was graduated from the School of Pharmacy in Chongqing University in 2020. In the same year, she joined the School of Geographic and Biologic Information in Nanjing University of Posts and Telecommunications. The research interests focus on bioinformatics, artificial intelligence, and omics data analysis, conducting researches on biomarker discovery, drug target discovery, and developing bioinformatics tools such as web-server and software. At present, a total of 20 SCI articles have been published, of which 10 articles are published as the first or co-first author in such journals as Nucleic Acids Res, Brief Bioinform, J Proteomics, CNS Neurosci Ther and Int J Mol Sci. She works as a peer reviewer for such journals as Comput Biol Med, Front Pharmacol, Front Genet, Evid Based Complement Alternat Med, and so on.

Speech Title: "Algorithm Research and Online Tool Development for Omics Data Analysis Based on Machine Learning"

Abstract: The appropriate diagnostic biomarkers are the source of disease diagnosis. However, there is a serious problem for biomarker discovery, the instability of biomarkers severely limits the clinical application. In response to this key scientific issue, machine learning algorithms combined with omics data analysis could be used to improve the stability of biomarkers. In this research, firstly, a novel strategy using machine learning method was developed for discovering potential diagnostic biomarkers. This strategy could improve the robustness of biomarkers, which is useful in the biomarker discovery for pituitary tumors and schizophrenia. Using this strategy, the accuracy and reproducibility of biomarkers were significantly improved in contrast to traditional algorithms. Secondly, a new data processing strategy integrating sample normalization and metabolite normalization was proposed for metabolomic data. Based on this strategy, the online tool NOREVA (https://idrblab.org/noreva/) was constructed for analyzing and evaluating multi-class and time-course metabolomic data. Thirdly, an alignment method using mass-to-charge ratio and retention time for integrating large-scale metabolomic datasets have been developed, greatly improving the statistical significance of metabolic markers. Using this strategy, the online tool MMEASE (http://idrblab.org/mmease/) was developed for large-scale metabolomics including data integration and metabolite annotation. In summary, the strategy, machine learning algorithms combined with omics data could improve the stability of biomarkers for clinical application.

Prof. Shuguang Yuan

Chinese Academy of Sciences, China

Prof. Shuguang Yuan obtained his master degree in biochemistry and structural biology from the Shanghai Institute of Organic Chemistry (SIOC), Chinese Academy of Sciences in 2009. Following that, his doctoral dissertation was funded by the Maria Curia Fellowship. It was conducted in three different institutes in Europe: EPFL(Switzerland), Polish Academy of Science (Poland) and KULeuven University (Belgium). In June 2013, he was awarded with a PhD title with the honour of distinguished thesis. Prof. Yuan worked at Idorsia (previously known as Actelion) Pharmaceutical Ltd in Basel as a specialist in computer-aided drug design (CADD) for 5 years. In the past few years, he has advanced two of his designed molecules into clinical trials. In 2019, Prof. Yuan was offered a full professor position by the Shenzhen Institute of Advanced Technology, Chinese Academy of Science.

Speech Title: "Advancing Modern Drug Discovery via Computational Biotechnology"

Abstract: Modern drug discovery is a long and tedious process which costs at least 10 years and $2 billion dollars. How to speed up this expensive process has become one of the most essential topics in pharmaceutical industry. With the progresses in both artificial intelligence and computational biology, advancing modern drug discovery via computational pharmacy plays more and more important roles. In this talk, Prof. Yuan will talk about ultra-efficient computational drug discovery which includes new drug target identification, computational high throughput screening, homology modelling, large scale drug-like compounds, lead optimization, binding energy calculation, ADMET predication, toxicity prediction and others. In addition, Prof. Yuan will also talk about his successfully story on how to advance “first-in-class” drug molecules into clinical trials.

Prof. Xiaogen Zhou

Zhejiang University of Technology, China

Xiaogen Zhou, Ph.D., graduated from the School of Information Engineering, Zhejiang University of Technology in 2018. From 2018 to 2021, he worked as a postdoctoral fellow at the University of Michigan. Currently, he is a professor at Zhejiang University of Technology. His research interests include structural bioinformatics, computational intelligence, and deep learning. He has proposed a series of protein conformation optimization methods based on the abstract convexity theory, presented the first multi-domain protein structure assembly method through structural analogous templates, and developed the first fully-automated multi-domain protein structure assembly server DEMO. He also participated in 3 NIH and NSF million dollar projects. The DEMO server has assembled more than 3,000 multi-domain proteins for users from more than 40 countries. In the past five years, he has published 25 papers on high-level journals, including Nature Computation Science, PNAS, Bioinformatics, IEEE TEVC and TCYB. He has also applied 15 invention patents, of which 10 have been authorized. In 2019, he was awarded the excellent Doctoral Dissertation award of Zhejiang Province. He also participated in and won the silver Award of the 6th China International "Internet +" College Students Innovation and Entrepreneurship Competition (2020), and the second prize of the 16th "Challenge Cup" National College Students Extracurricular Academic Science and Technology Works Competition (2019).

Speech Title: "Progressive Assembly of Multi-Domain Protein Structures from cryo-EM Density Maps"

Abstract: Progress in cryo-electron microscopy (cryo-EM) provided potentials for large-size protein structure determination. However, the success rate for solving multi-domain proteins remains low due to the difficulty in modeling inter-domain orientations. We developed DEMO-EM, an automatic method to assemble multi-domain structures from cryo-EM maps through a progressive structural refinement procedure combining rigid-body domain fitting and flexible assembly simulations with deep neural network inter-domain distance profiles. The method was tested on a large-scale benchmark set of proteins containing up to twelve continuous and discontinuous domains with medium-to-low-resolution density maps, where DEMO-EM produced models with correct inter-domain orientations (TM-score >0.5) for 97% of cases and significantly outperformed the state-of-the-art methods. DEMO-EM was applied to the SARS-CoV-2 coronavirus genome and generated models with average TM-score/RMSD of 0.97/1.3Å to the deposited structures. These results demonstrated an efficient pipeline that enables automated and reliable large-scale multi-domain protein structure modeling from cryo-EM maps.

Assoc. Prof. Xubo Lin

Beihang University, China

Xubo Lin received his B.S. degree in Applied Physics (2009) and Ph.D. degree in Biomedical Engineering (2014) from Southeast University, Nanjing, China. Subsequently, he went to McGovern Medical School of the University of Texas Health Science Center at Houston for postdoctoral training till the end of 2017. At the beginning of 2018, he joined Beihang University (BUAA) as an associate professor of Biomedical Engineering. His research interests include nanobiology, membrane biophysics and drug discovery.

Speech Title: "Revealing the Dynamics of Lipid Rafts and Its Biological Significance with Molecular Simulations"

Abstract: Due to differential interaction preferences of different lipids, the cell membrane can segregate into a series of nanoscale domains. Among them, ordered membrane domains are defined as “lipid rafts”, which were frequently reported to be important in various biological processes. In this talk, we will discuss our recent computational progresses on revealing the molecular mechanisms of intra-leaflet and inter-leaflet dynamics of lipid rafts as well as their correlations with the biological functions of the cell membrane. Especially, how the dynamics of lipid raft affects the local transmembrane potential and PD-L1 membrane orientation preferences will be shared. These insights from molecular simulations as well as validation experiments will be essential for elucidating biological roles of lipid rafts.

Prof. Yangzi Jiang

The Chinese University of Hong Kong, China

Prof. JIANG Yangzi is a Research Assistant Professor at institute for Tissue Engineering and Regenerative Medicine (iTERM), School of Biomedical Sciences (SBS), and Assistant Professor (by courtesy) at Department of Orthopaedics & Traumatology (ORT), The Chinese University of Hong Kong. She is the Young Chief Scientist of National Key Research and Development (R&D) Program, awarded by Ministry of Science and Technology (MOST) of China in Stem Cells Translational Medicine. Prof. Jiang’s research interest include Development of therapeutic strategies for musculoskeletal tissue regeneration, and the disease mechanism of osteoarthritis and other degenerative and inflammatory diseases.

Speech Title: "Bioinformatics Analysis of Site Mutations in TrkA Protein Binding Efficacy and Downstream Signaling - a Clinical Based Rare Disease Case Study"

Abstract: The prediction of protein-protein interaction using bioinformatics tools provided potentials to explore the effect of amino acids alteration on protein conformation and function. Tropomyosin receptor kinase A (TrkA) is a membrane receptor and encoded by neurotrophic tyrosine receptor kinase 1 (NTRK1). Point mutations in NTRK1 caused genetic disease with multi-system disorders such as congenital insensitivity to pain with anhidrosis (CIPA). The molecular pathophysiological mechanism of TrkA site mutations in these patients in the skeletal system are unclear. Here, we introduce a research strategy combing bioinformatics methods of and bench work to clarify the effect of site mutation in TrkA on downstream protein-protein interactions, and provide the simulation and wet-lab evidence of co-immunoprecipitation to support the hypothesis thus provide insights to the potential pathological mechanism of the patients with TrkA mutations.