Danny Muzata

Biomedical Data Scientist · Computational Biologist · Bioinformatician

Dept. of Biological Sciences & Dept. of CSIS, BITS Pilani Hyderabad Campus

I study how proteins evolve under selective pressure — mapping the fitness landscapes behind antimicrobial resistance by combining structural biology, molecular dynamics, graph-based machine learning, and reinforcement learning.

Portrait of Danny Muzata Danny MuzataHyderabad, India
Protein Evolution Structural Biology Network Science & Graph Theory Machine Learning Drug Discovery
Research Highlights Milestones

Research Highlights

Illustration of a fitness landscape with a single dominant peak Preprint · bioRxiv 2026

PfDHFR Fitness Landscape

Epistatic network constraints shaping predictable antifolate resistance in P. falciparum DHFR.

 Illustration of a residue interaction network graph In Preparation

Residue Interaction Networks

Graph-theoretic analysis of MD-derived residue networks, testing an epistatic "ridge" hypothesis.

 Illustration of a neural network Ongoing

Protein–Ligand Modelling

Mutation-sensitive graph neural network for predicting protein–ligand binding.

 Illustration of virtual screening, candidate compounds converging on a target Ongoing

Virtual Screening

Structure-based docking and free-energy rescoring against a cryptic PfDHFR pocket.

 Illustration of a DNA double helix Published 2025

DHPS Evolvability

A novel evolutionarily critical substructure in a sulfa-drug target, IF 7.7.

 Illustration of an antibody targeting a cell-surface antigen Published 2025

MUC1-C/ED ADC Design

Structural & metabolomic analysis supporting antibody-drug conjugate design for pancreatic cancer.

 Illustration of a protein embedded in a lipid bilayer Ongoing

Membrane Interaction Dynamics

MD of protein–membrane systems, characterising composition-dependent permeabilisation.

 Illustration of a branching decision tree with one highlighted optimal path Ongoing

RL for Adaptive Evolution

Reinforcement learning over mutational pathways in protein fitness landscapes.

About Me

I am a biomedical data scientist with about three years of interdisciplinary research experience spanning infectious diseases, molecular biology, bioinformatics, and computational biology. My work has evolved from molecular epidemiology and phylogenetic analysis of pathogens toward questions in protein evolution, structural biology, and antimicrobial resistance — involving sequence analysis, molecular modelling, molecular dynamics, and biological network analysis. I have also explored graph-based machine learning for protein–ligand interactions and reinforcement learning for adaptive evolution. I'm interested in building on this foundation through comparative genomics, multi-omics, and data-driven approaches that connect biological questions with computational method development.

This research is carried out at BITS Pilani, Hyderabad Campus, in the laboratory of Dr. Sourav Chowdhury (Dept. of Biological Sciences, Evolutionary Systems Biophysics), with a parallel collaboration in the laboratory of Dr. Prajna D. Upadhyay (Dept. of CSIS, Machine Learning & AI for Computational Biology).

Originally from Lusaka, Zambia — currently based in Hyderabad, India.

Milestones

Selected milestones from my work on protein evolution, structural biology, and machine learning for antimicrobial resistance.

2026

Flagship preprint on bioRxiv

"Epistatic Network Constraints Shape Predictable Antifolate Resistance in Plasmodium falciparum Dihydrofolate Reductase," submitted to npj Antimicrobials and Resistance.

2025–26

MUC1-C–targeted ADC study under review

A preclinical MUC1-C–targeted antibody-drug conjugate study in pancreatic cancer, under review at Molecular Cancer Therapeutics.

2025–26

Insecticide-efficacy study under review

A study on the insecticidal effectiveness of nicotine against Anopheles mosquitoes, under review at Malaria Journal.

2025

DHPS evolvability published — IF 7.7

Co-authored work mapping dihydropteroate synthase evolvability, published in the International Journal of Biological Macromolecules.

2025

MUC1-C/ED structural study published

Co-authored structural insight into MUC1-C/ED mutations for antibody-drug conjugate design, published in Biochemical and Biophysical Research Communications.

2025

Graduate Forum talk at IndoML 2025

Presented research on machine-learning applications in drug-resistance modelling at the 6th Indian Symposium on Machine Learning.

Research Areas

Five complementary lenses on one underlying question — how proteins evolve, fold, and interact — that together shape every project below.

Illustration of a protein structure

Protein Science

Sequence, structure, and function — how mutations reshape a protein's behaviour.

Illustration of a fitness landscape

Structural Biology

Molecular dynamics and structural modelling of drug targets and their resistance mutants.

Illustration of a network graph

Network Science & Graph Theory

Residue interaction networks and genotype graphs that reveal epistatic structure.

Illustration of a neural network

Machine Learning

Graph neural networks and reinforcement learning applied to evolutionary and structural data.

Illustration of a data heatmap

Drug Discovery

Structure-based virtual screening translating these insights into candidate therapeutics.

Rotating cartoon structure of Plasmodium falciparum dihydrofolate reductase with its bound antifolate inhibitor
Plasmodium falciparum dihydrofolate reductase, the enzyme implicated in resistance to the antifolate antimalarials pyrimethamine and cycloguanil, shown in complex with a bound inhibitor.
Schematic of a variational graph autoencoder (VGAE): an encoder maps an input residue interaction graph to a latent distribution z, from which a decoder reconstructs the graph's edges — the graph-representation-learning approach behind the protein–ligand and residue-network models above.
Schematic of an explicit-solvent molecular dynamics system: the PfDHFR structure inside a periodic simulation cell, solvated by water molecules and neutralising Na⁺/Cl⁻ counter-ions — the kind of all-atom setup equilibrated before production MD runs.

Adaptive Walk on an Epistatic Fitness Landscape

A population evolving across sequence space doesn't take a direct route to higher fitness — epistasis carves the landscape into ridges and valleys, and an adaptive walk from wild type is channelled along a narrow set of accessible, compensatory trajectories rather than the shortest path uphill.

Illustration of a rugged fitness landscape with an adaptive walk from wild type along a latent compensatory ridge to a high-fitness peak
Schematic of a rugged fitness landscape, with an adaptive walk from wild type along a latent compensatory ridge to a high-fitness peak.
Contour plot of a free-energy landscape over the first two principal components of a molecular dynamics trajectory, coloured by relative free energy
Free-energy landscape (ΔG) over the leading principal components of an MD trajectory, contoured to reveal the system's metastable conformational basins.
3D surface plot of the same free-energy landscape, showing conformational basins and the barriers separating them
The same free-energy surface rendered in 3D, showing the conformational basins and the barriers separating them.

Research Interests

Publications & Research Contributions

Google Scholar (April 2026): 23 citations · h-index 2 · i10-index 1 · cumulative impact factor 13.7 · full list on Google Scholar · † first/co-first author · * corresponding author

Epistatic Network Constraints Shape Predictable Antifolate Resistance in Plasmodium falciparum Dihydrofolate Reductase Preprint

Danny Muzata†, D. Sanyal, D. Pandey, S. Chakraborti, V.N. Uversky, P. Upadhyay, S. Chowdhury*

bioRxiv, 2026.01.04.697492 (2026) · submitted to npj Antimicrobials and Resistance

bioRxiv

Project Overview

Understanding the Socio-economic and Environmental Determinants of Antimicrobial Resistance Book Chapter

R. Bhagawati, A. Banerji, E. Charani, D. Malghan, S. Lewycka, S. Goel, A. Giri, S. Jejeebhoy, K. Chhotaria, H. Kadlak, R. Laxminarayan, D. Muzata, V. Nampoothiri, D. Batheja* et al.

Cambridge University Press — Public Humanities: Antimicrobial Resistance — Just Transitions for Shared Futures (2026)

Insecticidal Effectiveness of Nicotine from Nicotiana tabacum Against Sub-species of Anopheles gambiae and Anopheles funestus from the Luapula Province of Zambia Under Review

N.C. Sande*, M. Simuunza, K. Muzandu, D. Muzata, C. Zimba, B. Hamainza, M. Mwenda, B. Mambwe, R. Ngwira, J. Mweene, M. Mudenda, E. Mwase

Under review, Malaria Journal · IF 3.0

Systemic Metabolic Reprogramming and Marked Tumor Regression in Pancreatic Cancer Mediated by a MUC1-C–Targeted ADC Under Review

R. Ahmad, G. Panchamoorthy, C. Prodhan, D. Pandey, D. Zielinski, B. Lawney, K. Mani, R. Verma, M. Jaggi, D. Muzata, S. Kharbanda, S. Chowdhury, R. Jasuja

Under review, Molecular Cancer Therapeutics · IF 6.9

Mapping Dihydropteroate Synthase Evolvability Through Identification of a Novel Evolutionarily Critical Substructure Published

D. Sanyal, A. Shivram, D. Pandey, S. Banerjee, V.N. Uversky, D. Muzata, A. Chivukula, R. Jasuja, K. Chattopadhyay, S. Chowdhury*

International Journal of Biological Macromolecules 311, 143325 (2025) · IF 7.7

Project Overview

Effects of Mutations on MUC1-C/ED Protein Stability and Antibody Binding: Structural Insight Published

D. Sanyal, D. Muzata†, V.N. Uversky, S. Kharbanda, S. Chowdhury*, R. Jasuja

Biochemical and Biophysical Research Communications 775, 152114 (2025) · IF 3.4

Project Overview

Association Between Complete Blood-Count-Based Inflammatory Scores and Hypertension in Persons Living With and Without HIV in Zambia Published

L. Mwape, B.M. Hamooya, E.L. Luwaya, D. Muzata, K. Bwalya, C. Siakabanze, et al.

PLoS ONE 19(11), e0313484 (2024) · IF 2.6

Diseases Transmitted to Humans Through Foodborne Microbes in the Global South Book Chapter

M.O. Oduoye, K.A. Akilimali, A.A. Karim, A.A. Moradeyo, Z.Z. Abdullahi, D. Muzata, et al.

In: Food Safety and Quality in the Global South, pp. 561–597 (2024), Springer, Singapore

View Chapter

Gender Differences in Knowledge of Genetic Disabilities and Attitudes Towards Genetic Testing and Counselling in Zambia Published

K.K. Muzata, G. Walubita, D. Muzata, M.M. Sefotho, M. Mofu, O. Chakulimba

Journal of Educational Research on Children, Parents and Teachers (2020)

Presentations, Workshops & Training

Conference talks and posters

Selected workshops & training

Research Groups

My research spans two groups at BITS Pilani, Hyderabad Campus — linking wet-lab and structural biophysics with computational method development.

Portrait of Dr. Sourav Chowdhury

Dr. Sourav Chowdhury — Department of Biological Sciences

Chowdhury Lab · Evolutionary Systems Biophysics

Structural and computational biophysics of protein evolution — fitness landscapes, epistasis, and evolvability in drug-resistance and cancer-target proteins.

Portrait of Dr. Prajna D. Upadhyay

Dr. Prajna D. Upadhyay — Department of Computer Science and Information Systems

Upadhyay Lab · ML & AI for Computational Biology

Machine learning and artificial intelligence methods for biological problems — reinforcement learning for adaptive evolution and graph neural networks for protein–ligand interaction prediction.

My Resume

2020 – 2023
MSc Tropical Infectious Diseases & Zoonosis (ASIIN Accredited) — University of Zambia, Great East Road Campus, Lusaka
Thesis: Molecular Survey of Zoonotic Babesia Species in Ticks Collected from Cattle in Zambia
2021 – 2022
Postgraduate Diploma in Bioinformatics (First Class with Distinction) — JSS Academy of Higher Education and Research, Mysuru, India
2014 – 2019
BSc Biomedical Sciences (Graduated with Merit) — University of Zambia, Ridgeway Campus, Lusaka
Dissertation: Detection of HPV in Retinoblastoma Biopsies at the University Teaching Hospital, Lusaka

Contact Me

Email:
muzatadanny@gmail.com
p20230078@hyderabad.bits-pilani.ac.in

Location:
Dept. of Biological Sciences, BITS Pilani Hyderabad Campus, India
Originally from Lusaka, Zambia

BITS Pilani, Hyderabad Campus
One Health Trust — Bengaluru